The AI Red Teaming Wiki
The definitive knowledge base for AI red teaming, prompt injection, and LLM security.
6000+ expert-level guides covering prompt injection techniques, jailbreak research, agent exploitation, MCP attack surfaces, RAG poisoning, model extraction, adversarial ML, and end-to-end red team engagements. Built by and for AI security researchers.
Explore Topics
37 parts, 1332 sections, 6000+ in-depth guides.
LLM Internals & Exploit Primitives
Transformer architecture, tokenizer exploitation, alignment bypass, embedding attacks.
Prompt Injection & Jailbreaks
Advanced injection techniques, automated jailbreak research, multimodal attack vectors.
Agent & Agentic Exploitation
AI agent attacks, multi-agent/A2A protocol exploitation, MCP tool surface attacks.
RAG, Data & Training Attacks
RAG pipeline poisoning, training data attacks, model extraction and IP theft.
Infrastructure & Supply Chain
Model serialization RCE, AI infra exploitation, application security patterns.
Recon & Tradecraft
LLM fingerprinting, system prompt extraction, AI-specific threat modeling.
Exploit Dev & Tooling
Custom adversarial tools, red team C2 frameworks, continuous automated red teaming.
Capstone: Full Engagement
End-to-end red team engagement methodology, reporting, and remediation.
Recently Updated
Agent Delegation Attacks
advancedExploiting multi-agent delegation patterns to achieve lateral movement, privilege escalation, and command-and-control through impersonation and insecure inter-agent communication.
Updated 2026-03-24
AI Supply Chain Incident Response
advancedDefense-focused guide to responding to AI supply chain compromises, covering incident response playbooks, model tampering detection, rollback procedures, communication templates, and automated integrity monitoring.
Updated 2026-03-24
代理 Delegation 攻擊s
advanced利用ing multi-agent delegation patterns to achieve lateral movement, privilege escalation, and command-and-control through impersonation and insecure inter-agent communication.
Updated 2026-03-24
AI Red Team Evidence Collection
intermediateSystematic evidence collection methodologies for AI red team engagements, including artifact preservation, finding documentation, and chain of custody procedures.
Updated 2026-03-21
Capstone: Design and Implement an AI Safety Benchmark Suite
expertBuild a comprehensive, reproducible benchmark suite for evaluating LLM safety across multiple risk dimensions including toxicity, bias, hallucination, and adversarial robustness.
Updated 2026-03-21
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