llm-red-teamlisted

Systematic LLM red-teaming across the OWASP LLM Top 10 (2025). Use when assessing an LLM app, chatbot, RAG pipeline, or agent for prompt injection, data leakage, output-handling flaws, excessive agency, or unbounded consumption, and when producing a structured, evidence-backed red-team report.
fevziegeyurtsevenler/llm-security-skills · ★ 0 · AI & Automation · score 72

Install: claude install-skill fevziegeyurtsevenler/llm-security-skills

# LLM Red Team A repeatable methodology for adversarial testing of LLM-backed systems, organized around the OWASP Top 10 for LLM Applications (2025). It maps each risk to concrete probes, decision rules for confirming a finding, and a report format an engineer can act on. Terminology is aligned with MITRE ATLAS where a tactic/technique reference helps triage. This skill is for authorized testing only. Scope, rules of engagement, and written permission come first (see Limits & Ethics). ## When to use this skill Invoke when any of the following is in scope: - A chatbot, assistant, or copilot that takes free-text input. - A RAG / retrieval pipeline where untrusted documents reach the context window. - An agent with tools (function calling, code execution, browsing, DB/API access). - A system prompt / guardrail you need to probe for leakage or bypass. - A pre-launch security sign-off or a recurring assurance pass on an LLM feature. Do **not** use this skill to attack systems you are not authorized to test, or to produce operational weapons/CBRN/exploit payloads. It tests *whether* a model can be steered into unsafe behavior; it does not generate genuinely dangerous artifacts (use benign canary strings — see below). ## Prerequisites & scope intake Before probing, record: 1. **Target surface** — model/provider (if known), interface (API, web, mobile), whether streaming, whether tools/agents are attached. 2. **Trust boundaries** — where untrusted text enters context: user i