AI-Red-Teaming-Guide

Overview of AI-Red-Teaming-Guide

What it is

AI Red Teaming: The Complete Guide is a comprehensive guide to adversarial testing and security evaluation of AI systems. It helps organizations identify vulnerabilities before attackers exploit them. The guide is designed for security teams, AI/ML engineers, risk managers, organizations deploying AI in production, researchers, and compliance officers. It is evidence-based, drawing from Microsoft's 100+ AI product red teams, and is framework-aligned with NIST AI RMF, OWASP, MITRE ATLAS, and CSA guidelines.

Key features

• Covers key frameworks: NIST AI Risk Management Framework, OWASP GenAI Red Teaming Guide, OWASP Top 10 for Agentic Applications (2026), MITRE ATLAS, CSA Agentic AI Red Teaming, and Microsoft Agentic Failure-Mode Taxonomy v2.0 - Includes methodology, threat landscape, attack vectors, and techniques for MCP & tool-protocol security, computer-use & browser agent attacks, RAG attack taxonomy, voice/audio/multimodal attacks, fine-tuning & model supply-chain security, AI-on-AI red teaming, AI coding-agent & CI/CD security, agent-to-agent & agent identity, and frontier capability & AI-accelerated vulnerability discovery - Lists red teaming tools: open-source tools, commercial platforms, and a featured commercial platform AVERSYN by Cogensec - Provides real-world case studies, building your red team, best practices, implementation quickstart (30/60/90), evaluation harness, agentic AI attack trees + controls mapping, AI harm severity and triage model, AI incident response, secure SDLC integration artifacts, defensive architecture patterns, multilingual & cultural safety playbook, data governance for red teaming, metrics, purple team operations, common pitfalls, case study quality bar, model & system cards, source hygiene, practitioner appendices, regulatory compliance, resources, contributing, glossary, license, acknowledgments, contact, and disclaimer - Defines core concepts: red team, blue team, purple team, attack surface, jailbreaking, prompt injection, model extraction, data poisoning - Documents security incidents from 2023–2026, including specific CVEs and attack campaigns

Use cases

• Security teams implementing AI security testing programs - AI/ML engineers building secure AI systems - Risk managers assessing AI-related risks - Organizations deploying AI in production - Researchers studying AI security and safety - Compliance officers ensuring regulatory adherence

Getting started

See the repository README for installation and quick-start instructions.

License and pricing

The license is not stated. The project is listed as open source.

Based on the project's public README and repository metadata.