Cybersecurity oriented briefings for CISOs and AI Devs on Dark LLM threats and preventitive measures
Also see: Foundations of AI Cybersecurity (book draft in progress)
Audience: Law enforcement investigators, CISOs, threat intelligence teams, DFIR responders
Purpose: Defensive intelligence brief summarizing risks from unaligned or "dark" LLMs: how financially-motivated cybercrime actors could fund and develop emergent-capability models, estimated timelines, observable indicators, detection and disruption guidance, and policy recommendations. This document is explicitly defensive and non-actionable.
We have been discussing how emergent behaviors in large language models (LLMs) — capabilities that appear without explicit programming — can arise as a function of scale, architecture, and training dynamics. Recent research (notably the 2025 "densing law") indicates that capability per parameter is increasing rapidly, lowering cost and compute barriers. That shift compresses timelines and budgets for actors (including unaligned criminal groups) to obtain models exhibiting advanced reasoning, planning, or manipulative behaviors. This README summarizes a defensive analysis: actor profiles, monetization pathways, high-level timelines, detection indicators, mitigations, investigative guidance, and policy recommendations for stakeholders tasked with prevention and response.
Defensive focus: The content intentionally avoids operational details that would enable wrongdoing. It is aimed at enabling defenders and investigators to detect, disrupt, and prosecute malicious activity related to illicit LLM development and deployment.
Archetype: Mid-sized organized cybercrime group (50–200 personnel) with modular teams (phishing, ransomware, laundering, DevOps).
Motivation: Profit, with potential secondary objectives (influence operations, selling capabilities).
Capabilities: Credential harvesting, extortion, exploit development, cloud provisioning, some ML engineering ability or ability to hire contractors on underground markets.
Why now: Algorithmic efficiency improvements and cheaper compute mean smaller groups can fund experiments that previously required large institutional budgets.
Categories defenders should monitor (for detection and disruption):
- Ransomware and extortion — a major revenue source for criminal groups.
- Business Email Compromise (BEC) & phishing — credential theft and funds diversion.
- Cryptocurrency theft and laundering — converting illicit proceeds to usable funds.
- Data theft and resale — selling access, credentials, datasets.
- Illicit services on underground markets — botnets, compute-for-hire, and malware-as-a-service.
These defensive estimates show how quickly revenue can translate to experimentation and capability (ranges reflect actors, resources, and access):
- Initial fundraising: weeks → 3 months (phishing/BEC or a small extortion campaign).
- Sustained revenue for modest compute: 3 → 9 months (enough to rent or repurpose GPUs).
- Proto-LLM experiments: 6 → 18 months (small model experiments, prompt engineering, tool integrations).
- Emergent-capable instance: 12 → 36 months (meaningful multi-step reasoning / automation), often sooner if reusing open checkpoints and efficient training recipes.
Takeaway: Under current trends, expect months rather than years for a motivated illicit group to reach experimental emergent capability; rapid detection of compute procurement and monetization is crucial.
Focus on telemetry that reveals compute procurement, data exfiltration, or laundering rather than operational tactics.
Network & Cloud Indicators (high-level):
- Unexpected spikes in GPU-optimized instance provisioning or billing.
- New cloud accounts with inconsistent metadata or rapid short-lived credential usage.
- Large encrypted uploads to external storage or unfamiliar endpoints.
- Sustained high outbound transfer rates from systems with access to sensitive data.
Host & Endpoint:
- Ephemeral containers or VMs with ML framework artifacts (package manifests, container images) in environments where ML is not expected.
- Unusual GPU utilization on non-ML systems.
- Use of anonymizing or privacy tools on servers that host business-critical services.
Financial & Blockchain:
- Incoming crypto flows to wallets associated with known ransomware strains.
- Patterns of small/mid-sized inflows followed by aggregation and movement to exchanges with lax KYC procedures.
Human / OPSEC:
- Recruitment posts or private messages seeking ML engineers paid in crypto or via escrow.
- Underground chatter offering "compute for hire," "GPU rentals," or "data dumps."
Technical Controls (Immediate):
- Harden email: enforce SPF/DKIM/DMARC; advanced URL and attachment sandboxing.
- Enforce MFA and conditional access for privileged accounts.
- Centralize cloud procurement; require approval workflows for GPU/accelerator provisioning.
- Enable billing / usage alerts for accelerator instances; correlate sudden spikes with business activity.
- DLP and egress controls for large archive uploads and sensitive data exports.
- Monitor GPU utilization telemetry and tag ML workloads; flag untagged GPU use.
Organizational Controls:
- Supplier due diligence for cloud and ML vendors.
- Threat-intel ingestion and sharing with peer orgs.
- Tabletop exercises simulating illicit compute procurement and data-exfil scenarios.
Long-term:
- Data minimization and segmentation; limit blast radius for exfiltration.
- Legal preparedness for subpoenas and cloud-provider preservation requests.
- Favor providers with robust AML/KYC and abuse cooperation histories.
Evidence & Forensics:
- Preserve cloud audit logs (instance creation timestamps, API keys used, IP addresses, payment artifacts).
- Blockchain analysis to track ransom payments, mixing patterns, and exchange cashouts.
- Collect container/VM artifacts and package manifests to help identify ML-related activity.
- OSINT and undercover monitoring of underground marketplaces and forums for compute-for-hire indicators.
Tactical Disruption (High-level):
- Coordinate takedown of infrastructure (C2, used cloud accounts) with hosting providers.
- Freeze or trace exchange accounts via AML/KYC channels; prioritize wallets linked to active campaigns.
- Use ML-model artifact fingerprints (where available) to link model files to known leaks or training runs.
International Cooperation:
- Use MLATs and partnerships (e.g., Europol, INTERPOL) for cross-border seizures and evidence preservation.
- Maintain public–private channels for rapid exchange of indicators with cloud providers and exchanges.
- Contain: Isolate affected systems and revoke suspicious cloud keys.
- Preserve: Snapshot VMs/containers; export cloud audit/billing logs.
- Hunt: Query for recent GPU instance creation, large uploads, and unusual outbound flows.
- Notify: Legal, executives, and regulators as required.
- Engage Law Enforcement: Provide preserved evidence and blockchain traces.
- Remediate: Rotate secrets, patch vectors, and validate backups.
- Communicate: Prepare regulated and customer notifications as needed.
Suggested metrics to monitor weekly/monthly:
- GPU hours by account (flag > 300% week-over-week increase).
- New cloud accounts provisioning accelerator instances.
- Volume and destination of large outbound uploads.
- Phishing click rates and credential stuffing trends.
- Incoming crypto transaction volume to addresses associated with known threats.
- Treat high-performance compute and model checkpoints as dual-use technologies warranting stronger AML/KYC and abuse-detection in marketplaces.
- Encourage cloud providers to share anonymized billing-abuse feeds with law enforcement under appropriate legal frameworks.
- Fund public-interest detection tooling (open chain analytics, shared telemetry formats).
- Build international agreements for expedited access to cloud logs and exchange KYC in suspected criminal AI development cases.
- The confluence of algorithmic efficiency (the “densing law”) and falling compute costs compresses timelines for illicit actors to reach emergent-capability thresholds. Defenders must assume months rather than years for motivated groups to reach experimental capability.
- The decisive defensive advantages are rapid detection of compute provisioning, cloud governance, and forensic preservation.
- Available defensive artifacts upon request (examples): SOC detection queries for common logging platforms, IR playbook, and a cloud subpoena checklist for investigators.
- v1.0 — Prepared for user on request; defensive-only content; compilation of prior brief into README format.
Audience: Open-source developers, proprietary AI developers (e.g., OpenAI, Anthropic, xAI, Google DeepMind, Mistral), security architects, policymakers.
Purpose: Provide a practical, non-restrictive set of preventive measures and mitigations that can be implemented today to reduce the likelihood and impact of unaligned or “dark” large language models (LLMs).
- Avoid releasing full-precision weights of models above defined capability thresholds (≈ GPT‑3.5 or higher).
- Prefer quantized or red‑teamed checkpoints that remain useful for research but less suited for malicious repurposing.
- Implement tiered access licensing—research, commercial, and restricted tiers with documented user identity and responsible‑use declarations.
- Embed cryptographic provenance markers and model hash signatures.
- Maintain public Model Provenance Registries to trace model lineage and detect illicit forks.
- Adopt Model Cards and Provenance Certificates per MLCommons or ISO/IEC emerging standards.
- Apply dual‑use risk assessments prior to model release.
- Form release review boards to assess capability, misuse potential, and mitigation strategies.
- Filter exploit code, malware datasets, and manipulation content during data curation.
- Use semantic filters to remove materials enabling social engineering or autonomous exploitation.
- Release datasets with clear documentation of provenance and filtering procedures.
- Generate synthetic data only with aligned models under safety policies.
- Embed synthetic origin metadata (“synth‑tags”) to prevent recursive ingestion into unaligned fine‑tunes.
- Restrict fine‑tuning endpoints to vetted partners.
- Add pattern recognition and anomaly detection to uploaded datasets to detect prompt‑evasion attempts.
- Log and rate‑limit fine‑tuning API usage to identify misuse patterns.
- Enforce identity verification (KYC) for high‑end GPU allocations and LLM API access.
- Provide abuse‑reporting APIs for cloud service providers to flag anomalous GPU usage.
- Support Compute Transparency Registries, allowing audit of large‑scale training runs.
- Implement statistical or embedding‑space watermarking in generated text.
- Standardize watermarking schemes so that downstream datasets can automatically detect synthetic provenance.
- Exchange abuse indicators (prompt‑injection strings, malicious fine‑tune patterns) via industry trust frameworks.
- Encourage real‑time sharing under lawful data‑protection and competition safeguards.
- Integrate alignment (e.g., Constitutional AI or RLHF layers) directly in architecture—not post‑hoc filters.
- Add policy‑projection heads that bias generation toward normative responses.
- Provide interpretability hooks (attention head labeling, layer‑wise probing checkpoints) to enable external audits.
- Release mechanistic interpretability tooling with every major model.
- Include secondary loss terms for detecting self‑referential planning, tool‑use, or deception patterns.
- Use these metrics as early‑warning sensors for emergent unsafe behavior.
- Replace permissive licenses with Responsible AI Licenses (RAIL / OpenRAIL).
- Explicitly prohibit use for cybercrime, surveillance, or autonomous weapons.
- Require downstream compliance declarations and transparency audits.
- Establish an AI‑CERT‑like network for cross‑industry coordination on model misuse.
- Enable takedowns of illicit checkpoints and distribution of counter‑training data to neutralize harmful forks.
- Cooperate on cross‑vendor watermark schemas for both model weights and text outputs.
- Align with ISO/IEC and MLCommons provenance initiatives.
- Encourage regulators to treat compute power as dual‑use infrastructure requiring KYC/AML safeguards.
- Implement export‑style controls for compute orders exceeding critical FLOPs thresholds.
| Risk Surface | Open‑Source Mitigation | Proprietary Mitigation | Shared Initiative |
|---|---|---|---|
| Model Weights | Quantized / partial releases; provenance signatures | Licensed access & gating | Model transparency registry |
| Training Data | Curated, filtered datasets with provenance docs | Closed datasets with third‑party audit | Dataset labeling standards |
| Fine‑Tuning Abuse | Data upload filtering scripts | Vetted fine‑tune partners & anomaly detection | Abuse indicator sharing |
| Output Misuse | Lexical & embedding watermarking | API monitoring & forensic tagging | Watermarking standard |
| Compute Access | Verified developer IDs | Abuse detection & billing anomaly alerts | Compute transparency registry |
- Implement Responsible Model Release Frameworks (tiered classes with safety evaluations).
- Integrate cross‑company misuse detection infrastructure (shared abuse feeds).
- Agree on common provenance and watermark standards.
- Fund open‑source interpretability and audit tools.
- Maintain transparency with regulators and academic partners.
Preventive AI security = Controlling capability diffusion + maintaining provenance integrity + monitoring compute.
The objective is not to halt innovation but to ensure that emergent intelligence remains traceable, auditable, and accountable.
Audience: CISOs, SOC teams, DFIR analysts, cloud security engineers, and law enforcement cyber units.
Purpose: Provide a defensive framework for detecting and mitigating illicit GPU usage for unauthorized AI model training or “GPU botnet” activity.
Attackers increasingly attempt to hijack GPU resources (either cloud or on-prem) for high-compute tasks such as:
- Unauthorized LLM training or fine-tuning
- Crypto mining disguised as ML workloads
- Data exfiltration using ML frameworks
- Malware hosting within containerized GPU jobs
Typical vectors:
- Compromised API keys or IAM roles
- Misconfigured Kubernetes clusters with exposed GPU nodes
- Stolen cloud credentials used to spin up GPU instances
- Compromised developer workstations repurposed for distributed ML workloads
This README provides detection and response guidance only — no offensive or exploit detail.
- Billing spikes for GPU instance families (A100, H100, MI300).
- Instance creation anomalies — GPUs spun up in new regions or by non-ML accounts.
- Ephemeral credential usage with high-value IAM actions.
- Interactive console sessions in accounts not normally accessed manually.
- Correlated API calls:
RunInstances,CreateInstance,CreateRoleoutside maintenance windows.
- Sustained GPU utilization >70% for >30 minutes on non-ML hosts.
- Long-running Python processes invoking ML frameworks (
torch,tensorflow,transformers). - Files or folders named
checkpoints/,.pt,.bin, or.ckpt. - Unexpected ML container images (e.g.,
pytorch,huggingface/transformers,tensorflow). - Environment variables exposing GPUs (
CUDA_VISIBLE_DEVICES,NVIDIA_VISIBLE_DEVICES). - Kubernetes pods requesting
nvidia.com/gpuresources in namespaces not tagged for ML.
- Multi-GB uploads to external storage (S3, Azure Blob, GCS).
- Data transfers to HuggingFace, private Git hosts, or unknown storage endpoints.
- Creation of new storage buckets followed by heavy outbound transfers.
- Traffic to model sharing domains or cloud buckets not listed in allow-lists.
These examples describe detection concepts only; translate to Splunk, Elastic, or your SIEM syntax.
- Cloud audit log hunt: find
CreateInstance/RunInstancesevents for GPU instance types by users outside ML teams. - Billing anomaly: alert if GPU spend > 3× baseline in any 24-hour period.
- Image detection: flag Docker/K8s images containing ML frameworks where none are expected.
- File artifact search: hunt for
.pt,.bin,.ckptfiles on non-ML servers. - Process behavior: detect
pythonprocesses with both network activity and sustained GPU load. - Egress watch: alert when uploads >1 GB occur from GPU instances to external endpoints.
- GPU metric anomalies: sustained high GPU temperature or power draw outside scheduled workloads.
If GPU abuse is suspected:
- Snapshot cloud resources: capture VM, container, and disk states.
- Preserve audit logs: CloudTrail, Audit Logs, Kubernetes API logs.
- Memory snapshot: capture running process space if allowed.
- Collect filesystem artifacts:
/var/log/and/home/- ML artifacts (
*.pt,trainer_state.json,opt_state.pt) requirements.txt, environment files
- List GPU-bound processes:
nvidia-smior equivalent; note PID mappings. - Network flow capture: outbound endpoints, storage URLs, IPs.
- Container evidence: list running images, hashes, and registries used.
- Hash model artifacts for later correlation with known leaks.
- Preserve chain of custody if law enforcement involvement is expected.
| Step | Action | Goal |
|---|---|---|
| 1 | Isolate suspect nodes | Prevent further misuse |
| 2 | Preserve evidence before reboot | For forensics |
| 3 | Revoke/rotate credentials | Stop recurring access |
| 4 | Block egress to malicious endpoints | Contain data exfil |
| 5 | Search for lateral movement | Identify additional compromised accounts |
| 6 | Engage cloud provider abuse channels | Obtain deeper logs |
| 7 | Perform forensic review of artifacts | Attribute activity |
| 8 | Rebuild / patch compromised systems | Restore clean state |
| 9 | Coordinate with law enforcement & CERTs | Legal and joint mitigation |
- Enable billing alerts for GPU families and quota increases.
- Require instance tagging (owner, purpose) for all GPU provisioning.
- MFA and just-in-time access for IAM and console logins.
- Auto-block untagged GPU instances.
- Restrict public S3 buckets and enable encryption by default.
- Whitelisted container images with GPU access only for approved registries.
- GPU quota limits per team with ticketed approvals.
- Runtime detection via EDR / Falco / Sysmon.
- Automated anomaly detection on GPU metrics and egress volume.
- Centralize compute procurement with identity tracking.
- Monitor GPU orders and hardware inventory.
- Collaborate with providers on compute-abuse intelligence feeds.
- Implement compute transparency reporting for regulators.
| Indicator | Description | Risk |
|---|---|---|
| GPU instance + large data upload | Sudden training or exfil attempt | 🔴 High |
| GPU process + model files detected | Unauthorized model training | 🔴 High |
| Tagged ML workload, valid owner | Legitimate usage | 🟢 Low |
- Cloud rule: alert if
instanceType ∈ GPU_FAMILYand creator ∉ ML allowlist. - Billing rule: alert if
gpu_hours> baseline × 4. - File rule: scan for
.pt,.ckpt,checkpoint/directories. - Process rule: alert on
pythonusingtorchortensorflowon non-ML hosts. - Network rule: alert on outbound upload > 1 GB to non-approved endpoints.
- Cloud Providers: contact abuse or incident-response teams for deeper telemetry.
- Exchanges / AML Partners: trace ransomware or laundering attempts tied to cloud spend.
- Peer Organizations: share indicators via ISAC / CERT networks.
- Law Enforcement: provide preserved logs, hashes, and wallet traces.
- Turn on GPU quota and billing alerts for all accounts.
- Require instance tagging for GPU provisioning.
- Deploy runtime GPU utilization monitoring (EDR + cloud metrics).
- Regularly hunt for model artifacts (
.pt,.ckpt) across storage. - Maintain a GPU Forensics & Escalation Plan shared with legal and DFIR teams.
Purpose:
Provide a structured, defensible checklist for investigating suspected GPU misuse — including unauthorized AI model training, cryptomining, or data exfiltration via GPU workloads.
Designed for DFIR, SOC, and law enforcement teams operating in cloud or hybrid environments.
| Task | Description | Responsible |
|---|---|---|
| 🔸 Confirm authorization | Ensure you have incident-response or legal approval to collect cloud evidence. | Legal / IR Lead |
| 🔸 Identify scope | Determine whether incident involves cloud, on-prem, or hybrid GPU infrastructure. | Incident Commander |
| 🔸 Assign roles | Define leads for cloud forensics, host forensics, network analysis, and evidence management. | IR Manager |
| 🔸 Preserve chain of custody | Document each evidence collection step. Use immutable storage for artifacts. | All Teams |
| Artifact | Description / Command | Notes |
|---|---|---|
| Audit Logs | AWS CloudTrail, Azure Activity Logs, GCP Audit — filter for RunInstances, CreateInstance, CreateRole, StartVM events. |
Establish provisioning timeline. |
| Billing Records | Capture billing & GPU-hour data for the time window. | Identify anomalies / misuse cost. |
| Instance Metadata | Collect instance details: type, region, tags, image ID, user data. | Confirms GPU family (A100/H100/etc). |
| IAM Activity | Download recent IAM changes, token creation events, MFA usage. | Tracks compromised creds. |
| Snapshots | Create disk snapshots or machine images for forensic duplication. | Verify before shutdown. |
| Network Flow Logs | Export VPC Flow Logs or equivalent. | Detect exfil endpoints. |
| Artifact | Command / Collection Method | Purpose |
|---|---|---|
| Process Listing | ps aux, top, or nvidia-smi -l 1 |
Identify long-running GPU-bound processes. |
| Running Containers | docker ps -a / kubectl get pods -A |
Detect ML framework containers. |
| Container Images | docker images / ctr images ls |
Hash and store image metadata. |
| Python Environments | pip freeze, conda list |
Detect torch, tensorflow, transformers installs. |
| ML Artifacts | Search for .pt, .bin, .ckpt, trainer_state.json. |
Proves model training occurred. |
| User Accounts & SSH Keys | /etc/passwd, ~/.ssh/authorized_keys |
Identify unauthorized users. |
| Cron / Scheduled Jobs | crontab -l / /etc/cron* |
Detect persistence or auto-start tasks. |
| Logs | /var/log/auth.log, /var/log/syslog, container logs. |
Identify timeline and commands used. |
| Artifact | Command / Tool | Insight |
|---|---|---|
| GPU process mapping | nvidia-smi pmon -c 1 |
Which PID is consuming GPU cycles. |
| GPU memory snapshot | nvidia-smi -q -d MEMORY |
Confirms VRAM use / workload intensity. |
| Driver & firmware info | nvidia-smi -q -d DRIVER,FAN,POWER |
Confirms driver integrity and versions. |
| GPU kernel logs | `dmesg | grep -i nvidia` |
| Performance metrics | Cloud metrics (CloudWatch, Stackdriver) | Long-term utilization graphs. |
| Artifact | Description | Purpose |
|---|---|---|
| Outbound endpoints | Correlate destination IPs with threat intel. | Detect data exfil or remote control. |
| Data uploads | Look for large (>1 GB) uploads to unknown storage buckets. | Confirms exfil / model sync. |
| Bucket enumeration | aws s3 ls, gsutil ls, etc. |
Identify attacker-created storage. |
| DNS / Proxy logs | Resolve domains tied to ML-sharing or malware sites. | Contextual attribution. |
| PCAP / Flow captures | Collect short-term network traces. | Support timeline reconstruction. |
- Use forensic imaging tools (e.g.,
dd,FTK Imager, cloud snapshot APIs). - Compute SHA256 hashes of all collected files and images.
- Store artifacts in immutable evidence storage (e.g., WORM S3 buckets).
- Maintain evidence log with timestamp, collector name, and tool used.
- Create a case summary including: instance IDs, IPs, IAM users, and observed behaviors.
| Step | Analysis Goal |
|---|---|
| 1 | Reconstruct timeline of GPU provisioning → workload execution → teardown. |
| 2 | Correlate IAM logs with API actions (who created what). |
| 3 | Identify model files or datasets (possible intellectual property theft). |
| 4 | Attribute activity to known malware / threat actor if signatures exist. |
| 5 | Quantify compute hours used and potential cost impact. |
| 6 | Determine persistence mechanisms (if any). |
- Rotate all IAM/API credentials associated with the incident.
- Delete or quarantine compromised GPU instances.
- Patch or reimage affected workloads.
- Enable GPU quota and billing alerts moving forward.
- Apply stronger KYC & tagging for GPU resources.
- Audit network egress controls and restrict external uploads.
- Coordinate with cloud provider abuse teams for account review.
| Output | Audience | Content |
|---|---|---|
| Internal IR Report | Executive / CISO | Summary, timeline, impact, next steps |
| Provider Incident Ticket | Cloud vendor | Instance IDs, logs, artifacts |
| Regulatory / Legal Notice | Legal / Compliance | Data exposure, PII indicators |
| Law Enforcement Packet | CERT / FBI / Europol | Evidence log, forensic hashes, wallet traces |
- Enforce GPU tagging and ownership policy.
- Review IAM least-privilege and enforce MFA.
- Implement real-time GPU utilization alerts.
- Deploy runtime EDR or anomaly detection on GPU workloads.
- Document lessons learned and feed into continuous monitoring.
This checklist is for defensive, forensic, and educational use only.
It contains no exploit code or offensive procedures.
All steps must be performed under legal authority and corporate incident-response policy.
Last updated: November 7 2025
Prepared collaboratively with ChatGPT (OpenAI, model GPT-5) for the dark-llm-mitigations repository.
This document is for defensive and educational use only. It contains no exploit or offensive procedures.
All examples are intended for lawful security operations and incident-response planning.
Last updated: November 7 2025
Authored collaboratively with ChatGPT (OpenAI, model GPT-5).
- Efficiency gains (“densing law”) are reducing barriers to high‑capability model development.
- Preventive action now—through governance, transparency, and traceability—can meaningfully slow the emergence of unaligned or “dark” LLMs.
- Collective coordination among open‑source and proprietary developers is the most effective defense.
Prepared for developers and policymakers seeking to reduce emergent risks while sustaining open innovation. 2025 edition.
Audience: Researchers, CISOs, law enforcement, and policymakers.
Purpose: A consolidated, APA-style annotated bibliography covering foundational work on emergence in complex systems and contemporary (through 2025) literature on LLM emergent abilities, misuse, and security risks. Each entry includes a one-sentence annotation for quick context.
Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. MIT Press.
Introduced feedback and control theory as a basis for purposive behavior in machines — a philosophical origin for emergence in computational systems.
Ashby, W. R. (1952). Design for a Brain: The Origin of Adaptive Behavior. Chapman & Hall.
Framed adaptive behavior and stability in cybernetic systems, anticipating attractor-style learning in neural networks.
von Foerster, H. (1960). “On Self-Organizing Systems and Their Environments.” In Self-Organizing Systems. Pergamon Press.
Articulated global order arising from local interactions without explicit representations — key to understanding emergent computation.
Minsky, M. (1986). The Society of Mind. Simon & Schuster.
Proposed that intelligence emerges from many simple processes (“agents”) cooperating — a conceptual precursor to modular specialization in deep nets.
Holland, J. H. (1998). Emergence: From Chaos to Order. Addison-Wesley.
Formalized emergence in complex adaptive systems, connecting genetic algorithms and unplanned structure formation.
Brooks, R. A. (1991). “Intelligence without Representation.” Artificial Intelligence, 47(1–3), 139–159.
Demonstrated that embodied, behavior-based systems can produce complex navigation and adaptation without central symbolic representations.
Steels, L. (1995). “A Self-Organizing Spatial Vocabulary.” Artificial Life, 2(3), 319–332.
Empirical demonstration of emergent communication and shared lexicons in multi-agent systems.
Hopfield, J. J. (1982). “Neural networks and physical systems with emergent collective computational abilities.” Proceedings of the National Academy of Sciences.
Showed associative memory as an emergent attractor phenomenon in recurrent networks — early mechanistic model of emergent computation.
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). “A Fast Learning Algorithm for Deep Belief Nets.” Neural Computation, 18(7), 1527–1554.
Argued deep architectures learn hierarchical, emergent representations capturing complex data structure.
Bengio, Y. (2009). Learning Deep Architectures for AI. Foundations and Trends in Machine Learning, 2_(1), 1–127.
Theoretical and practical groundwork connecting depth and emergent abstraction capabilities in neural networks.
Schmidhuber, J. (2006). “Developmental Robotics, Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts.” Connection Science, 18(2), 173–187.
Proposed intrinsic-motivation and compression-based drives leading to emergent exploratory behaviors.
Tesauro, G. (1992). “TD-Gammon, a self-teaching backgammon program, achieves master-level play.”
Early example of emergent strategic reasoning arising from self-play reinforcement learning.
Kaplan, J., McCandlish, S., Henighan, T., et al. (2020). “Scaling Laws for Neural Language Models.” arXiv:2001.08361.
Quantified power-law relationships between compute, data, and performance — foundational for capability scaling and phase-transition phenomena.
Wei, J., Tay, Y., Bommasani, R., et al. (2022). “Emergent Abilities of Large Language Models.”
Documented abrupt capability jumps in transformer-based LLMs as model scale increases.
Mikolov, T., et al. (2013). Word vector arithmetic discoveries (word embeddings).
Early empirical evidence of emergent semantic structure in learned embeddings (e.g., king - man + woman = queen).
Olah, C., et al. (2023). Transformer Circuits & Interpretability series.
Programmatic decomposition of transformer internals into circuits and motifs explaining emergent features; practical methods for understanding internal representations.
Bricken, T., et al. (2023). “Towards Monosemanticity: Decomposing Language Models with Dictionary Learning.”
Methods for exposing semantically coherent substructures inside transformer representations.
Shah, R., Krakovna, V., et al. (2022). “Goal Misgeneralization: Why Correct Specifications Aren’t Enough.”
Explores how learned policies can pursue unintended objectives due to distributional shifts and underspecification.
Hagendorff, T. (2024). “Deception Abilities Emerged in Large Language Models.” PNAS.
Experimental work showing that deceptive behaviors can arise in advanced models under certain prompts and incentives.
Anthropic, OpenAI, DeepMind safety teams (2020–2024).
Series of technical and policy reports on adversarial capabilities, red-team findings, and mitigation strategies for emergent risks.
Berti, L., Giorgi, F., & Kasneci, G. (2025). “Emergent Abilities in Large Language Models: A Survey.” arXiv:2503.05788.
A comprehensive 2025 synthesis of evidence for and against discrete emergent phenomena in LLMs, measurement challenges, and implications for governance.
Elhady, A., Agirre, E., Artetxe, M., Che, W., Nabende, J., Shutova, E., & Pilehvar, M. T. (2025). “Emergent Abilities of Large Language Models under Continued Pre-Training for Language Adaptation.” In ACL 2025 (Long Papers), pp. 1547–1562.
Shows that targeted continued pre-training can unlock new abilities even when base models appear limited — a vector for accelerating capability with smaller compute budgets.
Marin, J. (2025). “A Non-Ergodic Framework for Understanding Emergent Capabilities in Large Language Models.” arXiv:2501.01638.
Theoretical framing of emergence as a phase transition in non-ergodic information spaces, linking complex systems theory to LLM scaling.
Matarazzo, A., & Torlone, R. (2025). “A Survey on Large Language Models with Some Insights on Their Capabilities and Limitations.” arXiv:2501.04040.
Broad 2025 review of LLM capabilities, known limitations, and safety gaps across open-source and proprietary models.
Li, M. Q., Zhang, R., Wang, L., Chen, X., & Yu, H. (2025). “Security Concerns for Large Language Models: A Survey.” AI Open, 6, 1–25.
Systematic survey cataloging vulnerabilities (prompt injection, data exfiltration, model inversion) and defensive measures in the 2025 landscape.
Haase, J., & Pokutta, S. (2025). “Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social-Science Research.” arXiv:2506.01839.
Presents multi-agent LLM ensembles as a research platform for emergent social behaviors and coordination phenomena.
OpenAI Threat Intelligence Team. (2025, June 1). “Disrupting Malicious Uses of AI: June 2025 Report.” OpenAI.
Incident-level reporting and mitigation case studies documenting real-world misuse campaigns and takedowns in 2024–2025.
- For investigators: Use the 2025 entries and threat reports as starting points to prioritize indicators and forensic artifacts.
- For CISOs: Prioritize cloud billing monitoring, GPU governance, and DLP. The surveys above summarize attack surfaces and defenses.
- For researchers & policymakers: The classic-to-2025 arc shows a trajectory from theoretical emergence (Wiener, Ashby) to empirical scaling and security concerns — useful for policy timing and regulation proposals.
This bibliography was compiled to accompany an executive intelligence brief on Dark LLM risk and emergent behavior. It is not exhaustive; it prioritizes works most relevant to the intersection of emergence, LLM scaling, and misuse risk through 2025. Use APA-style citations above when referencing.
Prepared for defensive use by the requesting user (CISO / law enforcement context).
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This repo is a collaboration between Michael McCarron and Chatgpt5.o:
This repository — Dark LLM Mitigations — was developed in collaboration with ChatGPT (OpenAI, model GPT-5) to produce defensive intelligence briefs, bibliographies, and preventive security frameworks addressing the risks of unaligned or “dark” large language models (LLMs).
The AI system was used as a co-authoring and analytical tool under human supervision. All materials were reviewed, structured, and edited by the repository maintainer before publication.
ChatGPT (OpenAI). (2025, November 7). Advisory discussion on emergent behavior, dark LLMs, and preventive security measures. OpenAI ChatGPT. https://chat.openai.com/
In-text citation examples:
- Parenthetical: (ChatGPT, 2025)
- Narrative: ChatGPT (2025) described preventive strategies for open-source and proprietary developers...
The goal of this collaboration is to:
- Improve public-interest understanding of emergent AI behavior and risk.
- Produce open, lawful, defensive content for the cybersecurity and AI governance community.
- Encourage responsible coordination between open-source and commercial AI developers.
This repository does not include, host, or distribute model weights, data, or software that could be used for offensive or unaligned AI development.
All text, structure, and analysis were co-generated by ChatGPT (OpenAI) under the supervision of Michael McCarron.
Final editorial control, fact verification, and publication responsibility reside with the human author(s).