Open-Sourcing ModelAudit: Security Scanner for ML Model Files

ModelAudit is an open-source static security scanner for machine learning (ML) model files, designed to identify unsafe loading behaviors, known CVEs, and suspicious artifacts across 42+ formats without executing the models or importing ML frameworks. Developed by Promptfoo, it addresses the issue of model files executing code at load time, often overlooked by teams downloading models from public registries. ModelAudit provides comprehensive security checks, including CVE detection, SARIF output for CI/CD integration, and supports diverse formats like PyTorch, TensorFlow, and ONNX. Unlike existing blocklist-based scanners such as picklescan and Fickling, ModelAudit employs an allowlist-first approach to minimize false positives and bypasses, offering a lightweight, framework-independent tool for platform and application security teams. The scanner's development involved extensive testing and refinement, leading to its release as a standalone, MIT-licensed project that enhances security in the ML model ecosystem.

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