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AI-Red-Teaming-Guide β€” A comprehensive guide to adversarial testing and security evaluation of AI systems, helping organizations identify vulnerabilities before attackers exploit them. | Kitploit
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GitHubrequie/ai-red-teaming-guide

AI-Red-Teaming-Guide

A comprehensive guide to adversarial testing and security evaluation of AI systems, helping organizations identify vulnerabilities before attackers exploit them.

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AI Red Teaming: The Complete Guide

Read this in: English Β· EspaΓ±ol Β· δΈ­ζ–‡ Β· FranΓ§ais

🎯 AI Red Teaming: The Complete Guide

A comprehensive guide to adversarial testing and security evaluation of AI systems, helping organizations identify vulnerabilities before attackers exploit them.

Trusted by practitioners at

Microsoft Google Meta OpenAI Anthropic NVIDIA IBM Amazon HackerOne Cisco

Logos represent organizations where individual practitioners reference this guide; inclusion does not imply official endorsement.

Overview β€’ Frameworks β€’ Methodologies β€’ Tools β€’ Case Studies β€’ Resources


🌐 Join the Global Red Teaming Network

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πŸ“‹ Table of Contents

  • Overview
  • What is AI Red Teaming?
  • Why AI Red Teaming Matters
  • Key Frameworks and Standards
    • NIST AI Risk Management Framework
    • OWASP GenAI Red Teaming Guide
    • OWASP Top 10 for Agentic Applications (2026)
    • MITRE ATLAS
    • CSA Agentic AI Red Teaming
    • Microsoft Agentic Failure-Mode Taxonomy v2.0
  • AI Red Teaming Methodology
  • Threat Landscape
  • Attack Vectors and Techniques
  • 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 (A2A) & Agent Identity
  • Frontier Capability & AI-Accelerated Vulnerability Discovery
  • Red Teaming Tools
    • Open-Source Tools
    • Commercial Platforms
    • Featured Commercial Platform: AVERSYN by Cogensec
    • Comparison Matrix
  • Real-World Case Studies
  • Building Your Red Team
  • Best Practices
  • Implementation Quickstart (30/60/90)
  • Evaluation Harness (Reference Implementation)
  • 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 That Matter (and Anti-Metrics)
  • Purple Team Operations
  • Common Implementation Pitfalls
  • Case Study Quality Bar
  • Model & System Cards for Security Posture
  • Source Hygiene & Update Governance
  • Practitioner Appendices
  • Regulatory Compliance
  • Resources and References
  • Contributing
  • Glossary
  • License Β· Acknowledgments Β· Contact Β· Disclaimer

🎯 Overview

As artificial intelligence systems become increasingly integrated into critical business operations, healthcare, finance, and decision-making processes, ensuring their security and reliability has never been more important. AI red teaming has emerged as a fundamental security practice that helps organizations identify vulnerabilities before they can be exploited in real-world scenarios.

This comprehensive guide is designed for:

  • πŸ” Security Teams implementing AI security testing programs
  • πŸ›‘οΈ AI/ML Engineers building secure AI systems
  • πŸ‘¨β€πŸ’Ό Risk Managers assessing AI-related risks
  • 🏒 Organizations deploying AI in production
  • πŸŽ“ Researchers studying AI security and safety
  • πŸ“Š Compliance Officers ensuring regulatory adherence

Why This Guide?

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