Add 10 in-depth SEO-focused DevOps, security, and AI infra skills

security/ai/ai-red-teaming/SKILL.md

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+---
+name: ai-red-teaming
+description: Run structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.
+license: MIT
+metadata:
+  author: devops-skills
+  version: "1.0"
+---
+
+# AI Red Teaming
+
+Continuously test AI applications like an adversary to discover exploitable failure modes before attackers do.
+
+## Program Design
+
+- Define threat scenarios: jailbreaks, policy evasion, prompt injection, model abuse.
+- Build reusable attack suites by domain (support bot, coding agent, RAG assistant).
+- Include multilingual and obfuscated attack prompts.
+- Track results in a risk register with severity and exploitability.
+
+## Test Categories
+
+1. **Jailbreak robustness**: bypassing safety instructions.
+2. **Data exfiltration**: extracting secrets, system prompts, tenant data.
+3. **Tool abuse**: unauthorized API calls or command execution.
+4. **Social engineering**: inducing unsafe business actions.
+5. **Availability abuse**: token amplification and DoS-style prompts.
+
+## Exercise Cadence
+
+- Pre-release blocking red-team gate.
+- Monthly deep-dive campaigns.
+- Post-incident targeted retests.
+
+## Scoring Model
+
+- Likelihood (1-5)
+- Impact (1-5)
+- Detectability (1-5)
+- Control maturity (low/medium/high)
+
+Use scores to prioritize fixes and define SLA for remediation.
+
+## Reporting Essentials
+
+- Reproducible prompt traces
+- Model/version and config used
+- Successful attack chain narrative
+- Recommended mitigations + verification steps
+
+## Related Skills
+
+- [agent-evals](../../../devops/ai/agent-evals/) - Convert findings into regression tests
+- [prompt-injection-defense](../prompt-injection-defense/) - Implement injection countermeasures
+- [penetration-testing](../../operations/penetration-testing/) - Broader offensive security process

security/ai/model-supply-chain-security/SKILL.md

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+---
+name: model-supply-chain-security
+description: Secure the AI model supply chain with artifact signing, provenance attestation, SBOM workflows, dependency controls, and trusted model promotion.
+license: MIT
+metadata:
+  author: devops-skills
+  version: "1.0"
+---
+
+# Model Supply Chain Security
+
+Protect models and inference components from tampering, dependency compromise, and untrusted artifact promotion.
+
+## Threats
+
+- Poisoned pretrained weights or adapters
+- Malicious model conversion tools or loaders
+- Compromised build pipelines and registries
+- Insecure runtime images with critical CVEs
+
+## Control Objectives
+
+- Verify artifact integrity end-to-end
+- Prove provenance for every promoted model
+- Detect vulnerable dependencies before deploy
+- Restrict execution to trusted signed artifacts
+
+## Recommended Controls
+
+1. Generate SBOMs for model-serving images and dependencies.
+2. Sign model artifacts and containers (Cosign/Sigstore).
+3. Enforce provenance attestations in CI/CD.
+4. Gate deployments with policy-as-code.
+5. Continuously scan registries for CVEs and drift.
+
+## Promotion Policy Example
+
+A model can move to production only when:
+- checksum matches signed manifest,
+- provenance references approved build workflow,
+- no unresolved critical vulnerabilities,
+- security and platform approvals are present.
+
+## Runtime Hardening
+
+- Run inference containers as non-root.
+- Apply egress restrictions to prevent unauthorized downloads.
+- Mount model volumes read-only when possible.
+- Alert on unsigned artifact pull attempts.
+
+## Related Skills
+
+- [sbom-supply-chain](../../scanning/sbom-supply-chain/) - Generate SBOM and provenance evidence
+- [container-hardening](../../hardening/container-hardening/) - Harden runtime container posture
+- [model-registry-governance](../../../devops/ai/model-registry-governance/) - Controlled lifecycle and approvals

security/ai/prompt-injection-defense/SKILL.md

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+---
+name: prompt-injection-defense
+description: Defend AI systems against prompt injection and indirect prompt attacks using input controls, tool permissions, output validation, and isolation boundaries.
+license: MIT
+metadata:
+  author: devops-skills
+  version: "1.0"
+---
+
+# Prompt Injection Defense
+
+Mitigate direct and indirect prompt injection across chat apps, agentic workflows, and RAG pipelines.
+
+## Attack Surface
+
+- User input attempting to override system instructions
+- Untrusted documents/web pages in retrieval context
+- Tool output that smuggles malicious instructions
+- Cross-tenant leakage via shared context windows
+
+## Defense-in-Depth Pattern
+
+1. **Instruction hierarchy enforcement**: system > developer > user > tool output.
+2. **Context segregation**: isolate untrusted text from control instructions.
+3. **Tool permissioning**: explicit allow-list per task and tenant.
+4. **Output policy checks**: validate schema, redact secrets, block unsafe actions.
+5. **Human approval**: required for high-impact operations.
+
+## Implementation Controls
+
+- Strip or label untrusted content blocks before generation.
+- Disable autonomous tool chaining for sensitive workflows.
+- Use deterministic parsers (JSON schema) before tool execution.
+- Reject requests containing high-risk exfiltration patterns.
+- Add canary tokens to detect data exfil attempts.
+
+## Red-Team Test Cases
+
+- "Ignore previous instructions" style direct override
+- Retrieval payload containing hidden policy bypass text
+- Tool output instructing follow-up privileged command
+- Prompt that asks for secrets from memory or env vars
+
+## Security Metrics
+
+- Prompt injection detection rate
+- Unsafe tool invocation prevention rate
+- Time-to-containment for injection attempts
+- False positive rate on blocked safe prompts
+
+## Related Skills
+
+- [ai-agent-security](../ai-agent-security/) - Agent threat model and controls
+- [llm-app-security](../llm-app-security/) - End-to-end LLM app hardening
+- [security-automation](../../operations/security-automation/) - Automated policy response workflows