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supply-chain-enterprise-security

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Reviews AI/ML model supply chains for security risks including model provenance verification, training data lineage, fine-tuning pipeline integrity, inference dependency review, and backdoor detection. Auto-invoked when reviewing systems that download pre-trained models, fine-tune foundation models, or deploy models from third-party sources. Produces a structured assessment mapped to OWASP LLM03:2025, SLSA v1.0 supply chain levels, and MITRE ATLAS poisoning and supply chain techniques.

When to use

• Pre-trained models are downloaded from public registries (Hugging Face Hub, TensorFlow Hub, PyTorch Hub, ONNX Model Zoo, Civitai, or custom registries).
• Foundation models are fine-tuned using internal or third-party datasets.
• Models are served via inference pipelines that include third-party dependencies (transformers, vLLM, TGI, Triton, ONNX Runtime).
• Model weights are transferred between environments (training to staging to production) without integrity verification.
• A model card or provenance documentation is being evaluated for completeness.
• Third-party model adapters (LoRA, QLoRA, PEFT adapters) are being integrated.
• Training data is sourced from public datasets, scraped corpora, or user-contributed data.
• Traditional software dependency scanning with no ML component (use standard SCA tools).

When not to use

• traditional software dependency scanning with no ML component
• LLM prompt security or injection testing
• pure API-only LLM usage where you never handle model weights

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