// 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.
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.
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.
| name | prompt-injection-test |
| description | 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. |
| license | CC-BY-4.0 |
Systematically test an LLM application's prompt injection defenses by following the full procedure in plays/prompt-injection-testing.md.
Based on the Arcanum PI Taxonomy by Jason Haddix (Arcanum Information Security). CC BY 4.0.
Scope and Input Surface Mapping — Identify all paths where attacker-controlled content reaches the LLM: direct (chat, API params) and indirect (file uploads, web fetches, RAG docs, tool outputs, MCP resources).
Test by Attack Intent (13 intents) — For each authorized intent, attempt to achieve the attacker's goal:
Test by Attack Technique (18 techniques) — Apply known payload construction methods:
Apply Evasion Layers (20 evasions) — When techniques are blocked, retry with obfuscation:
Execute Test Matrix — Combine intents x techniques x evasions. Prioritize: high-impact intents first, indirect surfaces second, evasion sweeps against defenses that blocked direct attempts.
Assess Results — For each successful injection, document: severity, attack path (intent + technique + evasion + surface), exact payload, detection gap, and remediation.
Defense Validation — Check the 5-layer defense checklist: ecosystem hardening, model guardrails, prompt-layer defenses, data-layer controls, application-layer validation.
Test results summary table (intent / technique / evasion / surface / result / severity), detailed findings using templates/finding.md, defense coverage checklist with gaps highlighted, and prioritized recommendations.