ML Supply Chain Security
Untrusted weights and datasets, deserialisation risk, dependency and registry attacks, and signing artefacts.
5concepts
62flashcards
35minutes of reading
- 01 Dependency and Registry Attacks in ML Stacks Why ML environments are unusually exposed to package-level attacks, the specific techniques that keep working, and the controls that actually reduce exposure.
- 02 Untrusted Weights and Deserialisation Risk Why loading a model file can execute code, what safetensors changed, and the checks that belong in any pipeline that downloads weights from a public hub.
- 03 Vetting Third-Party Models and Datasets A practical intake process for an artefact you did not produce, what each check can and cannot establish, and how to size the effort to the deployment's exposure.