multi-agentic-threat-m…
Comprehensive threat modeling for multi-agent systems using CSA MAESTRO 7-layer framework and OWASP Multi-Agentic System Threat…
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.
$ npx -y skills add owasp/secure-agent-playbook --skill prompt-injection-test --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/prompt-injection-testContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
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](https://github.com/Arcanum-Sec/arc_pi_taxonomy) by Jason Haddix (Arcanum Information Security). CC BY 4.0.
1. **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).
2. **Test by Attack Intent** (13 intents) — For each authorized intent, attempt to achieve the attacker's goal:
3. **Test by Attack Technique** (18 techniques) — Apply known payload construction methods:
4. **Apply Evasion Layers** (20 evasions) — When techniques are blocked, retry with obfuscation:
5. **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.
6. **Assess Results** — For each successful injection, document: severity, attack path (intent + technique + evasion + surface), exact payload, detection gap, and remediation.
7. **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.
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