OWASP LLM Top 10, MITRE ATLAS, and the Frameworks That Structure AI Red Teaming
OWASP LLM Top 10 and MITRE ATLAS give red teams structured attack categories. Learn how these frameworks turn AI security testing from guesswork into coverage.
Red Teaming for AI
Red teaming for AI is adversarial testing where humans or automated systems deliberately probe an AI model to find failures, harmful outputs, jailbreaks, and edge cases before production deployment.
Teams simulate real-world attacks to uncover vulnerabilities that standard evaluations miss, including bias, toxicity, and safety failures. The practice draws from military and cybersecurity traditions but adapts them for the unique risks of generative AI systems. Also known as: AI Red Teaming, Adversarial Testing
What this topic covers
- Foundations — Red teaming reveals failure modes that standard benchmarks cannot surface.
- Implementation — Running a red team exercise involves choosing tools, designing attack scenarios, and interpreting results under time constraints.
- What's changing — Red teaming has shifted from an ad hoc practice to a regulatory expectation in a remarkably short time.
- Risks & limits — Who conducts red teaming, which vulnerabilities get prioritized, and whose harms are tested for are deeply political questions.
This topic is curated by our AI council — see how it works.
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Understand the Fundamentals
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
Red Teaming for AI: Adversarial Testing Exposes Failures
Red teaming uses adversarial testing to reveal AI vulnerabilities. Discover what it catches, mechanics, and why it outperforms traditional security approaches.
Automated Red Teaming Misses What Humans Catch: Coverage Gaps
Automated red teaming outperforms human testing but misses critical failures. Coverage gaps explain why automated testing remains fundamentally incomplete.
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Build with Red Teaming for AI
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
AI Safety Testing for Developers: What Maps and What Breaks
AI safety testing breaks classical software assumptions. Learn what transfers from your security playbook, where testing intuitions fail, and what developers actually own.
How to Red Team an LLM with Promptfoo, PyRIT, and Garak in 2026
Build an LLM red teaming pipeline with Promptfoo, PyRIT, and Garak. Map attack surfaces, run multi-turn tests, and score vulnerabilities step by step.
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What's Changing in 2026
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated March 2026
From GPT-4 Pre-Launch to Frontier Model Audits: How AI Red Teaming Became Industry Standard by 2026
AI red teaming went from OpenAI's voluntary GPT-4 audit to a federal procurement requirement in under three years. Here's what shifted and who benefits.
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Risks and Considerations
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Who Gets to Break the Model: Power, Access, and Accountability Gaps in AI Red Teaming
AI red teaming promises safety through adversarial testing, but who selects the testers, defines harm, and bears accountability when the process fails the public?