Model Safety MCP
Malicious or unsafe ML artifacts are a real supply-chain problem. There have already been real cases of malicious or suspicious models being discovered in the wild, including models hosted on public platforms and larger batches of unsafe AI/ML artifacts. See RL identifies malware ML model hosted on Hugging Face and Over 100 Malicious AI/ML Models Found on Hugging Face.
One of the biggest reasons this matters is deserialization risk. Many model formats, especially pickle-based and framework-specific formats, can execute code or invoke unsafe logic while being loaded or reconstructed. That means a model file can become an execution vector, not just a passive blob of weights.
model-safety is an MCP server for inspecting machine learning model artifacts before you load, ship, or trust them.
It is designed for practical triage:
- scan a local model file
- scan a downloadable model URL
- triage a whole directory of artifacts
- combine heuristic checks with dedicated scanners
- return normalized findings and concrete next actions
