ai-safety
claudeAI safety (distinct from security): harm modeling, safety evaluations, responsible red-teaming, bias/fairness, guardrail review, and responsible-AI governance.
6 skills · 1 agent
11
claude
mlops-security
Security for the ML lifecycle and infrastructure: ML supply chain (model/dataset provenance, unsafe deserialization), training/MLOps pipeline security, and model-serving hardening.
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Plugin mlops-security from marketplace awesome-claude-security in jassics/awesome-claude-security. Commands read from the repo's manifest — 1 month ago.
What it installs
Skills (3)
ml-pipeline-security-reviewReview the training / MLOps pipeline for security weaknesses — data-poisoning surface, feature-store and data-source trust, experiment-tracking and model-registry access control, secrets, and reproducibility/provenance. Use when assessing how models are built and promoted, not how they're served or
ml-supply-chain-reviewReview the provenance and integrity of models, datasets, and ML artifacts pulled from hubs/registries — unsafe deserialization (pickle, PyTorch/Keras/joblib), untrusted model sources, model/dataset tampering, and signing. Use when an app loads third-party model weights or datasets, or before promoti
model-serving-securityHarden a deployed model inference endpoint — authn/authz, rate limiting and abuse control, input validation, and exposure to model-extraction / inversion / membership-inference attacks. Use when reviewing how a model is served (REST/gRPC, Triton/TorchServe/KServe, or a hosted inference API), not how
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