requie/AI-Red-Teaming-Guide

What it solves

AI Red Teaming solves the challenge of proactively identifying security vulnerabilities in AI systems before they are exploited. As AI moves from simple chatbots to autonomous agents that can execute code, access files, and interact with infrastructure, traditional security testing is no longer sufficient. This guide provides a structured methodology to uncover novel failure modes, such as prompt injection, agent goal hijacking, and supply-chain attacks, helping organizations secure their AI deployments against real-world threats.

How it works

This guide adapts established cybersecurity practices—like those from NIST, OWASP, and MITRE—to the unique, non-deterministic nature of AI. It provides a comprehensive framework for:

• Threat Modeling: Identifying how an attacker might manipulate an AI agent or model.
• Adversarial Testing: Using specific techniques to bypass safety guardrails and test system resilience.
• Integration: Embedding these security checks into the software development lifecycle (SDLC) and incident response plans.
• Continuous Evaluation: Using frameworks like the NIST AI RMF to monitor and manage risks throughout the system's lifecycle.

Who it’s for

• Security Teams tasked with building AI security programs.
• AI/ML Engineers responsible for developing and securing AI systems.
• Risk Managers evaluating the business and regulatory risks of AI adoption.
• Compliance Officers ensuring adherence to AI-specific regulations.

Highlights

• Evidence-based methodologies derived from extensive industry experience at major AI labs.
• Full alignment with major standards like NIST AI RMF, OWASP GenAI, and MITRE ATLAS.
• Practical, actionable guidance on testing for modern threats like agentic tool-use, RAG vulnerabilities, and supply-chain risks.
• A 30/60/90-day implementation quickstart guide for launching an AI red teaming program.
• Extensive resources including case studies, attack taxonomies, and defensive architecture patterns.