Welcome to redteams.ai
The AI security landscape is evolving faster than defenders can keep up. New model architectures, agentic frameworks, and deployment patterns create novel attack surfaces weekly. redteams.ai exists to be the definitive knowledge base for AI red teamers — from prompt injection fundamentals to cutting-edge multi-agent exploitation.
The Problem
AI systems are being deployed at unprecedented speed. Chatbots, autonomous agents, RAG pipelines, and multi-agent systems are moving from research prototypes to production deployments in weeks. Security assessment of these systems lags far behind.
The existing resources fall into two categories: shallow blog posts that name-drop attacks without explaining them, and academic papers that describe novel techniques without practical application guidance. Neither serves the working security professional who needs to assess real AI systems.
What We Cover
redteams.ai covers the full spectrum of AI red teaming:
- LLM Internals — Transformer architecture from a security perspective
- Prompt Injection — From basic injection to expert-level defense evasion
- Agent Exploitation — The expanding attack surface of autonomous AI agents
- Data Attacks — RAG poisoning, training data manipulation, and data extraction
- Infrastructure — Supply chain, API security, and deployment vulnerabilities
- Tradecraft — Methodology and reconnaissance skills for professional assessments
- Tooling — Building and using tools for scalable red teaming
- Capstone — Full engagement methodology from planning to reporting
Who This Is For
- Security professionals expanding into AI red teaming
- ML engineers who want to understand the security implications of their systems
- Red team leads scoping and planning AI security assessments
- Students and researchers building foundational knowledge in AI security
What's Next
We're actively expanding coverage across all topic areas. Upcoming content includes deep dives into MCP transport-layer attacks, multi-agent A2A protocol exploitation, and advanced jailbreak fuzzing techniques.
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