This guide provides a comprehensive framework for adversarial testing and security evaluation of AI systems, enabling organizations to proactively identify vulnerabilities before exploitation. It targets security teams, AI/ML engineers, and risk managers, offering practical methodologies and tools to enhance AI security and resilience.

How It Works

The guide details a structured AI Red Teaming methodology, encompassing planning, execution, evaluation, and reporting. It integrates key industry standards like NIST AI RMF, OWASP GenAI, and MITRE ATLAS, covering a broad spectrum of attack vectors including prompt injection, data poisoning, model extraction, and agentic AI exploits. The approach emphasizes simulating adversarial attacks to uncover novel failure modes and risks beyond traditional cybersecurity testing.

Quick Start & Requirements

As a comprehensive guide, direct installation is not applicable. Users can leverage the "Implementation Quickstart (30/60/90)" plan to establish an AI red teaming program. Key requirements involve understanding AI/ML fundamentals and security principles, with resources provided for skill development.

Highlighted Details

Key frameworks are integrated, including NIST AI RMF, OWASP Top 10 for Agentic Applications (2026), and MITRE ATLAS. The guide offers an extensive taxonomy of attack vectors and techniques, detailed methodologies, and a curated list of open-source and commercial red teaming tools. Real-world case studies from 2025-2026 and historical incidents illustrate practical application and lessons learned.

Maintenance & Community

Last updated in June 2026, the guide reflects current industry practices and research. It encourages community contributions and collaboration through the "Cogensec Global Red Teaming Network," fostering knowledge sharing among practitioners.

Licensing & Compatibility

The guide is released under the permissive MIT License, allowing for broad use, modification, and distribution with attribution. It is compatible with any organization seeking to implement AI security testing programs.

Limitations & Caveats

The guide emphasizes that all testing must be conducted with proper authorization, on systems owned or permitted for testing, and in compliance with laws and ethics. Unauthorized testing is illegal and unethical, requiring explicit permission before any exercises.