agent-security
AI Agent Security Architecture Review
This skill guides a structured security architecture review of AI agent systems -- applications where LLM-powered agents operate autonomously, invoke tools, maintain state, and potentially collaborate with other agents. The focus is on architectural security controls: permission models, containment boundaries, human oversight gates, auditability, and recoverability. The methodology is aligned with OWASP Agentic AI threat categories (from the OWASP GenAI Security Project) and NIST AI RMF 1.0.
This skill complements the agentic-top-10 skill (which covers the full OWASP Agentic AI threat taxonomy) by going deeper on architecture-level security controls. Use agentic-top-10 for a broad threat assessment; use this skill when the architecture itself needs detailed security review.
Limitations
- Blind spots: This skill depends on available code, configuration, logs, documentation, and user-provided context; it cannot prove controls exist or threats are absent when evidence is missing, runtime-only, or outside the review scope.
- False-positive risks: Treat findings as hypotheses until validated against asset criticality, compensating controls, environment intent, and recent authorized changes.
- Required evidence: Support each finding with concrete artifacts such as file paths and line numbers, policy snippets, scanner output, logs, screenshots, control records, or reproducible steps.
- Normalized JSON: When machine-readable output is requested, findings MUST be available as JSON that validates against
schemas/finding.schema.json. - Escalation rules: Escalate immediately for suspected active compromise, exposed secrets, regulated-data exposure, critical exploitable vulnerabilities, privileged-access abuse, or when evidence is insufficient to safely disposition a high-impact risk.