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Tarique Smith · June 10, 2026

AI Red Teaming: The Complete Guide

guide AI Red TeamingAgent SecurityModel Evaluation
Why it matters

Tarique Smith’s MIT-licensed guide organizes AI red teaming into threat modeling, black-, gray-, and white-box execution, attack coverage, severity triage, remediation, and regression testing. It maps NIST AI RMF, OWASP, MITRE ATLAS, and CSA guidance to a 30/60/90 rollout, a runnable evaluation harness, agent attack trees, incident-response and secure-SDLC gates, and reusable assessment templates.

My takeaway: Use the guide as a program scaffold, then tailor it to a concrete system and threat model. Start with scoped rules of engagement and crown-jewel assets, turn attack paths into versioned tests with expected outcomes, require reproducible evidence and named remediation owners, run the suite in CI, and revalidate cited frameworks and regulatory claims before treating the living repository as authoritative.
Keep exploring

More curated notes connected through AI Red Teaming and Agent Security.

OpenAI News · framework

OpenAI’s Frontier Governance Framework

OpenAI's 22-page Frontier Governance Framework maps its frontier-model processes to California's Transparency in Frontier AI Act and the EU AI Act's general-purpose AI code. It documents lifecycle risk assessment, cyber-offense and other risk tiers, mitigation and residual-risk decisions, critical-incident handling, security risk management, model reporting, external review, responsibility allocation, and change control.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

OECD.AI Wonk · guide

A five-step roadmap to closing the AI evaluation gap

The roadmap addresses evaluation results that overstate real-world performance or fail to transfer across deployment contexts. Its five steps balance standardized and local tests, evaluate throughout the lifecycle, build qualified assurance and communication capacity, tailor tests to each value-chain actor and technology, and use a coordinated, trusted process for updating methods.