weightguard

Security scanner for ML model artifacts. Point it at a Hugging Face repo, a git URL, or a local path, and it flags known artifact-level risks — malicious pickle/PyTorch payloads, Keras Lambda-layer code injection, ONNX custom-op RCE surface, and anomalous/crafted GGUF headers — with a severity and a concrete mitigation for each finding.

Model files are not inert data. Several common serialization formats can embed code that executes the moment the file is loaded, before any inference happens. weightguard is a static, pre-load check you run before trusting a downloaded model.

Install

pip install weightguard

Usage

# Hugging Face repo
modelsec scan https://huggingface.co/<org>/<repo>

# git repo
modelsec scan https://github.com/<org>/<repo>.git

# local path
modelsec scan /path/to/model

# CI gate — exit non-zero only above a severity threshold (default: HIGH)
modelsec scan <target> --fail-on CRITICAL

Exit codes: 0 clean, 1 a finding at/above --fail-on, 2 target could not be resolved.

What it checks today

Development

uv sync
uv run pytest -q

License

MIT