Directory context | AI Supply-Chain Security
ModelScan
Protection Against ML Model Serialization Attacks
Direct answer
What is ModelScan?
ModelScan is included in the Awesome MLSecOps AI Supply-Chain Security directory. The community-maintained README describes it as: “Protection Against ML Model Serialization Attacks.” Its MLSecOps relevance is the protection of model provenance, artifact integrity, dependencies, signing, bills of materials, registries, or delivery pipelines. The linked first-party source is the protectai/modelscan repository on GitHub. A technical review should test the project's documented evidence across four criteria: Provenance and signing support, ML-BOM formats, Registry and CI integration, and Policy enforcement. Compare that evidence with the intended architecture and threat model. Catalog inclusion establishes relevance to this security category; it is not a certification, comparative ranking, or endorsement. Confirm current capabilities, maintenance, licensing, limitations, and deployment assumptions in the first-party documentation before adoption.
Protection Against ML Model Serialization Attacks
Neutral catalog description synchronized from the Awesome MLSecOps README
Before adoption
What should teams verify about ModelScan?
Answer these questions from current first-party documentation and testing evidence rather than relying on the directory listing alone.
- 01
Which artifacts, identities, hashes, signatures, and provenance records are covered?
- 02
Which CycloneDX, SPDX, SLSA, Sigstore, or model-specific formats are supported?
- 03
Can evidence be verified across build, registry, conversion, and deployment boundaries?
- 04
How are trust roots, policy exceptions, key management, and failures handled?