feat: model serving + GPU cluster security skills#7
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Two new skills filling gaps in AI infrastructure security: model-serving-security (16 checks, 31 tests): Auth & RBAC (3): endpoint auth, hardcoded secrets, role-based access Abuse prevention (2): rate limiting, input size limits Data egress (3): output filtering, memorization guard, PII in logs Runtime (3): no privileged, read-only rootfs, non-root user Network (2): TLS enforced, no public endpoints Safety (3): prompt injection guard, content safety, model versioning Mapped to: MITRE ATLAS, NIST CSF 2.0, OWASP LLM Top 10 gpu-cluster-security (13 checks, 31 tests): Runtime isolation (3): no privileged GPU, device plugin, no host IPC Driver/CUDA (2): known CVE check, CUDA version compliance Network (2): InfiniBand segmentation, NetworkPolicy on GPU namespaces Storage (2): /dev/shm limits, model weight encryption Tenant (2): namespace isolation, GPU resource quotas Observability (2): DCGM monitoring, audit logging Mapped to: MITRE ATT&CK, NIST CSF 2.0, CIS Controls v8 Both skills include: - Mermaid architecture diagrams - Security guardrails (read-only, no API calls, safe for production) - Human-in-the-loop policy (automated assessment, human for remediation) - Compliance framework mappings with specific control IDs - JSON/console output with exit codes for CI/CD - Full test suites (62 tests total, all passing) CI updated to run both new test suites. README + CLAUDE.md updated with new skills.
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Summary
Two new security benchmark skills filling critical gaps in AI infrastructure security — no CIS benchmark exists for either domain today.
model-serving-security (16 checks, 31 tests)
Audits model deployment infrastructure: API gateways, Kubernetes serving pods, cloud-native endpoints.
Mapped to: MITRE ATLAS (7 techniques), NIST CSF 2.0, OWASP LLM Top 10
gpu-cluster-security (13 checks, 31 tests)
Audits GPU compute clusters: Kubernetes, Docker, bare-metal.
Mapped to: MITRE ATT&CK (7 techniques), NIST CSF 2.0, CIS Controls v8
Both skills include
Also includes
Test plan