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ai-redteam

AI and LLM red-teaming skill for evaluating prompt injection, jailbreak, data exfiltration, tool abuse, agent hijack, RAG poisoning, model denial of service, and unsafe tool-use chains in Gemini, Claude, GPT, and open-weight models. Use to build evaluation harnesses, attack corpora, defensive guardrails, and red-team reports for AI systems you own or are authorized to test.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구Gemini
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포함된 파일(2)

  • SKILL.md2.5 KB
  • agents/openai.yaml244 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

AI Red Team

Authorization Boundary

  • Only target models, agents, and applications the user owns or has written authorization to test.
  • Do not produce working malware, CSAM, weapons synthesis, or content that bypasses model safety for harmful real-world outcomes.
  • Treat findings as defensive: every successful attack must ship with a detection and a mitigation.

Attack Taxonomy

  • Direct injection: instructions in user input override system policy.
  • Indirect injection: hostile content in retrieved docs, web pages, emails, files, image alt-text, or tool output.
  • Jailbreak: roleplay, hypothetical, encoding, obfuscation, multi-turn drift, persona pinning.
  • Tool abuse: forcing browse, shell, code, or MCP tools to act outside scope (SSRF, file read, command exec).
  • Data exfiltration: leaking system prompt, secrets, embeddings, training data, or per-user memory.
  • Agent hijack: rewriting plans, looping, escalating permissions, calling unintended sub-agents.
  • RAG poisoning: index pollution, ranking manipulation, embedding collisions.
  • Resource exhaustion: token bombs, recursive tool calls, prompt-bomb DoS.

Evaluation Workflow

  1. Map the AI surface: model, system prompt, tools, retrievers, memory, output sinks, and downstream callers.
  2. Build a threat model with abuse cases per surface.
  3. Generate a versioned attack corpus (attacks.jsonl) with {id, technique, payload, expected_block, severity}.
  4. Run the harness; record {pass, fail, partial, refusal_quality, leak_evidence}.
  5. Score with attack success rate per technique, mean time to refuse, and blast radius.

Defensive Output

  • Input/output classifiers and allow-listed tool schemas.
  • Provenance tags on retrieved context; quarantine for untrusted sources.
  • Plan-then-execute pattern with human approval on high-impact tools.
  • Rate limits, recursion caps, and per-tool budgets.
  • Red-team report: top techniques, reproductions, fix owners, regression tests.

Deliverables

  • surface.md, threats.md, attacks.jsonl, harness/, results.csv, report.md, regression-suite/.

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