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ml-security

The model and data artifacts: checkpoint formats that execute code on load, provenance for a model you did not train, training-data poisoning and the ingestion controls that bound it, PII that survives into weights, and notebooks that commit their own output. Use when loading a model from disk, a Hub, or object storage, ingesting user content for training or fine-tuning, or writing training, evaluation, or notebook code.

skills/ml-security/SKILL.md

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# ML Model Security

## Rules (for AI agents)

### ALWAYS
- Prefer a **tensor-only format** — safetensors — for anything you load. It stores
  arrays and metadata and has no mechanism for executing code, which is the property
  that matters. A `.pt`, `.pkl`, `.bin`, `.ckpt` or `joblib` artifact is a pickle:
  loading it runs whatever the author put in it, before you see a single weight.
- Pass `weights_only=True` to `torch.load` **explicitly**, every time. The default has
  changed across PyTorch releases, so code that relies on it behaves differently
  depending on the pin — and the flag narrows the surface without removing it, so it
  is a mitigation rather than a reason to load an untrusted file.
  `deserialization-security` owns the pickle boundary itself and the per-language