claude

ai-red-teaming

AI/LLM red-teaming team — agents (ai-redteam-lead, adversarial-testing-engineer) for the layer answering 'can this AI system be made to do harm, leak data, or exceed its authority — and how do we harden it?': threat modeling + rules of engagement, the attack taxonomy (OWASP LLM Top 10 2025 + MITRE ATLAS), direct vs indirect prompt injection, jailbreaks (roleplay/encoding/many-shot/crescendo), data exfiltration & training-data extraction, agentic tool-abuse / excessive agency, multimodal attacks, and defense-in-depth remediation. Fluent in automated red-team harnesses (PyRIT, Garak, Promptfoo red-team, Giskard) and likelihood×impact severity. skills, a knowledge bank (attack-taxonomy decision tree + 2026 patterns), and templates. Distinct from llm-evaluation-engineering (quality-regression eval), trust-and-safety (content-moderation / T&S policy), and security-engineering (app/infra pentest) — the adversarial AI-security layer over model- and agent-based systems. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.2.2MITupdated today
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Plugin ai-red-teaming from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

What it installs

Skills (3)

  • design-ai-redteam-plan

    Scope an AI red-team engagement by threat-modeling the system (assets, attackers, trust boundaries), splitting safety from security, traversing the attack-taxonomy decision tree to a prioritized OWASP LLM Top 10 / MITRE ATLAS attack list, and setting the rules of engagement plus likelihood×impact su

  • harden-and-remediate-ai-system

    Triage red-team findings by likelihood×impact and drive defense-in-depth remediation — layered input/output guardrails, injection-resistant prompt structure, least-privilege tool scoping, allow-lists, human-in-the-loop on high-impact actions, output-handling hygiene, and rate/cost limits — then rete

  • run-adversarial-attacks-and-jailbreaks

    Execute the prioritized attacks against an AI system within the rules of engagement — direct and indirect prompt injection, jailbreaks (roleplay, encoding, many-shot, crescendo), training-data extraction and data exfiltration, agentic tool-abuse / excessive agency, and multimodal attacks — capturing

Subagents (2)

  • adversarial-testing-engineer

    Use to EXECUTE AI attacks & harden — run jailbreaks/prompt-injection/data-exfiltration, build automated red-team harnesses (PyRIT/Garak/Promptfoo), triage findings by likelihood×impact, drive defense-in-depth remediation. NOT for app/infra pentest (security-engineering).

  • ai-redteam-lead

    Use to SCOPE an AI red-team engagement — the threat model, rules of engagement, attack taxonomy (OWASP LLM Top 10 / MITRE ATLAS), the safety-vs-security split, and success/severity criteria. Decision-tree-driven. NOT for eval quality-regression testing (llm-evaluation-engineering).

View source on GitHub mcorbett51090.github.io

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