From ai-security-skills
Assesses agentic AI applications against OWASP Top 10 for Agentic Applications 2026, evaluating risks like goal hijacking, tool misuse, privilege abuse, and rogue agents. Use for reviewing autonomous AI agents, multi-agent systems, or workflows.
npx claudepluginhub cmaenner/agent-security-playbookThis skill uses the workspace's default tool permissions.
Evaluate agentic AI applications against all 10 risk categories by following the full procedure in `plays/tier4-ai-security/agentic-ai-risk-assess.md`.
Flags vulnerable patterns in autonomous LLM agents enabling irreversible actions without oversight. Suggests fixes like impact classification, tool allowlists, pre-dispatch auditing, and structured parameters for safe workflows.
Provides 6 AI engineering workflows: prompt evaluation (8D scoring), context budget planning, RAG pipeline design, agent security audit (65-pt checklist), eval harness building, product sense coaching. For LLM production systems.
Monitors deployed URLs for regressions after deploys, merges, or upgrades by checking HTTP status, console errors, network failures, performance (LCP/CLS/INP), content, and API health.
Share bugs, ideas, or general feedback.
Evaluate agentic AI applications against all 10 risk categories by following the full procedure in plays/tier4-ai-security/agentic-ai-risk-assess.md.
Architecture Mapping — Identify agent type (single/multi-agent), autonomy level, tool ecosystem, memory systems, inter-agent communication protocols, and human oversight mechanisms.
Assess Each Agentic Top 10 Risk:
Synthesize Findings — Assign severity based on exploitability and deployment context. Provide code locations, configuration gaps, and concrete remediation.
Architecture overview, risk matrix (all 10 categories with status), detailed findings using templates/finding.md, positive controls observed, and prioritized recommendations.