AI Red Teaming

Resources

GenAI Red Teaming Manual Version 1.0

The GenAI Security Project’s GenAI Red Teaming Manual provides practical guidance for evaluating the security, safety, and robustness of generative AI systems. Its eight-phase methodology covers planning, reconnaissance, attack surface mapping, exploitation, persistence, impact assessment, reporting, and remediation. Addressing models, applications, multimodal interfaces, and AI agents, it emphasizes ethical testing, reproducible findings, measurable outcomes, and […]

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Resources

AI Security Solutions Landscape For AI and Agentic Red Teaming Q2 2026

As organizations increasingly deploy generative AI and autonomous agents into business-critical workflows, traditional application security practices are no longer sufficient. AI systems introduce new classes of risk including prompt injection, model misuse, agent privilege escalation, data poisoning, hallucinations, and emergent behaviors that evolve continuously throughout the AI adoption lifecycle. Gen AI and Agentic Red Teaming

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Resources

OWASP Vendor Evaluation Criteria for AI Red Teaming Providers & Tooling v1.0

Vendor Evaluation Criteria for AI Red Teaming Providers & Tooling is a practical guide for organizations assessing vendors that offer AI red teaming services or automated testing tools. Developed under the OWASP GenAI Security Project, the document outlines clear criteria for evaluating both simple GenAI systems (such as chatbots and RAG applications) and advanced systems

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AI Red Teaming
& Evaluation

This project establishes comprehensive AI Red Teaming and evaluation guidelines for Large Language Models (LLMs), addressing security vulnerabilities, bias, and user trust. By collaborating with partners and leveraging real-world testing, the initiative will provide a standardized methodology for AI Red Teaming, including benchmarks, tools, and frameworks to boost cybersecurity defenses.

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Resources, Announcement

Announcing the OWASP Gen AI Red Teaming Guide

The OWASP Top 10 for LLM and Generative AI project , genai.owasp.org, team is thrilled to unveil the Gen AI Red Teaming Guide which provides a practical approach to evaluating LLM and Generative AI vulnerabilities—a new resource from our Red Teaming Initiative.  As Generative AI technologies like the Large Language Models (LLMs) evolve at breakneck speed, the

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Initiatives, Resources

GenAI Red Teaming Guide

This guide outlines the critical components of GenAI Red Teaming, with actionable insights for cybersecurity professionals, AI/ML engineers, Red Team practitioners, risk managers, adversarial attack researchers, CISOs, architecture teams, and business leaders. The guide emphasizes a holistic approach to Red Teaming in four areas: model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis.

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Jason Ross

Jason Ross is a passionate cybersecurity expert with a diverse skill set in generative AI, Penetration Testing, Cloud Security, and OSINT. As a product security principal at Salesforce, Jason performs security testing and exploit development with a specific focus on generative AI, Large Language Models, and Agentic systems. Jason is a frequent speaker at industry

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Jason Ross