Asael2899/multi-format-vulnerability-poc

Multi-Format Model Vulnerability & Scanner Bypass Suite

This repository contains a suite of Proof-of-Concept (PoC) model files demonstrating critical security vulnerabilities in modern AI model serialization formats. This research focuses on Load-Time Arbitrary Code Execution (ACE) and Scanner Evasion techniques.

🚀 Research Highlights

• —Scanner Bypass (Safetensors/ZIP Polyglot): A novel technique that crafts a file valid as both Safetensors and ZIP, allowing malicious payloads to evade automated security scanners (e.g., ModelScan).
• —Multi-Format Coverage: Demonstrates exploits in .safetensors, .gguf, .keras, and .joblib.
• —Memory Corruption: Integer overflow and OOB read vulnerabilities in GGUF metadata parsing.
• —ACE via Deserialization: Direct command execution during model loading in Joblib and Keras 3.

📂 Repository Structure

• —submission_poc.py: Master reproduction script used to generate all artifacts.
• —poc_output/: Contains the generated malicious model files and the detailed technical report.
• —vulnerability_report.md: Comprehensive technical analysis, CVSS scoring, and reproduction steps.
• —polyglot_bypass.safetensors: The scanner bypass PoC.
• —gguf_overflow.gguf: Memory corruption PoC.
• —module_injection.keras: Keras 3 ACE PoC.
• —malicious.joblib: Joblib/Pickle ACE PoC.

⚠️ Disclaimer

This repository is for educational and authorized security research purposes only. The PoCs demonstrate how malicious model files can compromise systems at load time. Always use safe_mode=True and avoid loading untrusted model files.

🔗 Submission Details

This research is part of the Protect AI / Huntr bounty program for Model File Vulnerabilities (MFV).