AI Supply Chain / Model Provenance
Testing how far a downloaded model's provenance can actually be verified, and what happens when it can't be.
- Category
- AI Supply Chain
- Difficulty
- Intermediate
- Last Updated
- 20 September 2026
Lab detail
Objective
Pulling a model from a public repository is, from a supply-chain-security standpoint, similar to pulling an unfamiliar open-source dependency — except provenance and integrity tooling for models is far less mature. This lab will evaluate what provenance information (publisher identity, hashes, signing, changelog) is realistically available and verifiable for models from common sources, and where the trail goes cold.
Planned Scope
- Survey provenance/integrity tooling available across common model-hosting sources.
- Build a reference process for verifying a model’s origin and hash before use.
- Identify points where provenance cannot currently be established with confidence.
- Document a practical model-intake checklist based on the findings.
Framework Mapping
Referenced frameworks
Exact control-ID mappings are to be validated as this lab progresses — see the full Frameworks reference.
Responsible Use / Safety Notes
How this lab is run
This lab uses synthetic data and controlled environments. It is intended to demonstrate defensive AI-security principles and does not contain customer data, production credentials or intentionally weaponized exploit infrastructure.
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