secure-agent-playbook

Security-focused review of native Android and iOS mobile app source code against OWASP MASVS v2.1.0. Use when reviewing mobile codebases, mobile PR diffs, or auditing a mobile module.

Audit AI agent configurations for security risks — excessive permissions, prompt injection surfaces, data exfiltration paths, and missing guardrails. Use when reviewing CLAUDE.md files, MCP configs, agent orchestration code, or any AI agent setup.

Assess agentic AI applications against the OWASP Top 10 for Agentic Applications 2026. Use when reviewing autonomous AI agents, multi-agent systems, or agentic workflows for security risks including goal hijacking, tool misuse, privilege abuse, and rogue agent behavior.

Comprehensive LLM security assessment against OWASP Top 10 for LLM Applications 2025. Use when reviewing LLM-integrated applications, RAG pipelines, chatbots, AI agents, or GenAI features. Covers prompt injection, data poisoning, supply chain, excessive agency, and more with real-world attack scenarios and testing methodologies.

Security review of MCP (Model Context Protocol) server implementations and configurations. Use when auditing MCP server source code, evaluating third-party MCP servers before installation, or reviewing Claude Code MCP integrations for overpermissioning, injection risks, and data exposure.

Test LLM-integrated applications against known prompt injection techniques, evasion methods, and attack intents using the Arcanum PI Taxonomy. Use when red-teaming AI apps, validating guardrails, or deepening LLM01 (Prompt Injection) assessments.

Comprehensive API security review against OWASP API Security Top 10 (2023). Use when reviewing OpenAPI/Swagger specs, auditing REST/GraphQL/gRPC implementations, testing authentication mechanisms, or checking API gateway configurations. Covers BOLA/IDOR, broken auth, mass assignment, rate limiting, SSRF, and more with real-world attack scenarios.

Security-focused code review mapped to OWASP Top 10 and ASVS. Use when reviewing pull requests, auditing files or modules for vulnerabilities, or performing pre-merge security gate checks. Covers injection, auth, authorization, cryptography, data exposure, misconfiguration, and deserialization.

Security review of Infrastructure-as-Code (Terraform, Kubernetes, CloudFormation). Use when reviewing IaC files for misconfigurations, overpermissioning, exposed resources, missing encryption, secrets in code, and supply chain risks. Covers CIS benchmarks and cloud security best practices.

Enhance PRDs, feature specs, user stories, or product briefs with explicit OWASP ASVS coverage and FIASSE v1.0.4 SSEM implementation guidance — before code is written. Trigger on "harden the PRD/spec", "choose ASVS level", "map features to ASVS", "find missing security requirements", "add NFRs for security", "make these requirements securable", "security-review my product brief". For code review use securability-engineering-review; for code generation use securability-engineering.

Scan project dependencies for known vulnerabilities (CVEs). Use when reviewing dependency files (package.json, requirements.txt, go.mod, pom.xml, Gemfile, Cargo.toml, etc.), triaging Dependabot/Renovate alerts, or performing pre-deployment security checks.

Detect hardcoded credentials, API keys, tokens, and secrets in source code and configuration files. Use when reviewing code for leaked secrets before commit/merge, auditing a repository for credential exposure, or setting up secret detection.

Score a codebase, file, or merge request against the FIASSE v1.0.4 SSEM model — 0-10 per attribute, equal-weighted pillars, evidence-backed strengths and weaknesses, prioritized recommendations, 50-item checklist appendix. Trigger on "review/score/audit securability", "SSEM scorecard", "FIASSE/SSEM compliance", "where would I start hardening this?", "is this audit-ready?", "security posture baseline" — including phrasings that don't say SSEM explicitly. For requirements use prd-securability-enhancement; for new code use securability-engineering.

Generate, scaffold, or refactor code so it embodies FIASSE v1.0.4 SSEM qualities by default — 10 attributes, Transparency and Least-Astonishment principles, ASVS-aligned controls, defensive boundary handling. Trigger on "secure/securable/FIASSE-compliant code", "harden", "secure-by-default", "audit-ready", or security-sensitive components (auth, file upload, password reset, input validation, API endpoints, queries) — even when those words are not explicit. For requirements use prd-securability-enhancement; for review use securability-engineering-review. The full PRD→generate→review→enhance loop is opt-in via "--full-loop" or "end-to-end securable".

Security-first development guidance based on OWASP ASVS (Application Security Verification Standard). Use this skill automatically when planning or implementing any code that touches user input, authentication, data persistence, network communication, file I/O, cryptography, or access control. This skill ensures all generated code adheres to industry-standard security practices with explicit references to applied guidance.

Review web applications against the OWASP Top 10 for Web Applications (2021). Use when auditing web apps, reviewing server-side code, or assessing web frameworks for the classic OWASP Top 10 risks including injection, broken auth, and XSS.

Comprehensive threat modeling for multi-agent systems using CSA MAESTRO 7-layer framework and OWASP Multi-Agentic System Threat Modeling Guide v1.0. Systematically analyzes threats across all architectural layers from foundation models to agent ecosystems.

Comprehensive AI security verification using OWASP AI Security Verification Standard (AISVS) framework. Provides structured checklist to verify security and ethical considerations across 13 categories of AI-driven applications, from training data governance to human oversight.

Comprehensive threat modeling for multi-agent systems using CSA MAESTRO 7-layer framework and OWASP Multi-Agentic System Threat Modeling Guide v1.0. Systematically analyzes threats across all architectural layers from foundation models to agent ecosystems.

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