OWASP Vendor Evaluation Criteria for AI Red Teaming

Summary

The OWASP Vendor Evaluation Criteria for AI Red Teaming Providers and Tooling gives procurement and security teams a structured method for assessing whether a red teaming vendor or tool can actually test the AI systems they need secured. Published by the OWASP GenAI Security Project in 2026, this document addresses a growing market problem: as demand for AI security testing has surged, vendors with varying degrees of genuine AI testing capability have entered the market. This guide helps organizations tell the difference between vendors that understand the specific attack surfaces of AI systems and those applying conventional security testing methods that miss AI-specific vulnerabilities entirely.

Why Standard Red Teaming Falls Short for AI

Traditional red teaming evolved to test network infrastructure, web applications, and human-process vulnerabilities. These methods remain valuable, but they were not designed for the attack surfaces that AI systems introduce. A conventional penetration test will examine whether an API endpoint is properly authenticated or whether input validation prevents SQL injection. It will not assess whether a language model can be manipulated through carefully crafted prompts to bypass its safety guidelines, exfiltrate training data, or produce harmful outputs.

AI systems introduce attack surfaces with no direct analog in traditional software. Prompt injection exploits the instruction-following nature of language models to override developer intentions. Data poisoning targets the training pipeline rather than the deployed system. Retrieval-augmented generation systems create new attack vectors where manipulating the knowledge base can alter model outputs without touching the model itself. Agentic systems that use tool calling and multi-step reasoning introduce risks around unauthorized actions, privilege escalation through natural language, and cascading failures across integrated services.

A vendor that cannot demonstrate specific expertise in these AI-native attack vectors is not equipped to provide meaningful AI red teaming, regardless of their credentials in traditional security testing.

Evaluation Criteria for Vendors

The guidance organizes vendor evaluation around several core dimensions that procurement teams should assess before engaging a red teaming provider.

Methodology and Approach

Vendors should articulate a testing methodology that specifically addresses AI attack surfaces. This includes prompt injection testing across multiple injection strategies, evaluation of model behavior under adversarial inputs, assessment of safety filter robustness, and testing of the system's ability to maintain alignment under sustained manipulation attempts. The vendor should demonstrate familiarity with published AI vulnerability taxonomies, including the OWASP Top 10 for LLM Applications, and show how their methodology maps to these known risk categories.

System Architecture Understanding

Effective AI red teaming requires understanding the full system architecture, not just the model endpoint. Vendors should demonstrate the ability to assess retrieval-augmented generation pipelines, including the security of vector databases, the integrity of document ingestion processes, and the potential for knowledge base poisoning. For agentic systems, vendors should show expertise in testing tool-calling mechanisms, evaluating permission boundaries, and assessing whether agents can be manipulated into taking unauthorized actions through conversational engineering.

Simple vs. Advanced System Testing

The guidance draws a distinction between testing simple AI applications and testing multi-component systems. Simple systems, such as a standalone chatbot with fixed guardrails, require a narrower testing scope focused on prompt injection, output safety, and information disclosure. Advanced systems involving RAG pipelines, multi-agent orchestration, Model Context Protocol integrations, or custom tool use require significantly broader testing that covers inter-component trust boundaries, data flow integrity, and emergent behaviors arising from component interactions.

Organizations should confirm their chosen vendor has demonstrated experience with the level of system complexity that matches their deployment. A vendor proficient at testing chatbots may not have the expertise needed for a multi-agent system that orchestrates across external APIs and internal databases.

Green Flags

Vendors that publish research on AI-specific vulnerabilities demonstrate genuine engagement with the field. Those that maintain or contribute to open-source AI security tools show practical technical depth. Vendors that can provide detailed case studies of AI-specific findings from previous engagements, rather than generic security reports, have likely conducted meaningful AI testing. Teams that include members with backgrounds in machine learning, natural language processing, or AI safety alongside traditional security expertise are better positioned to identify AI-native vulnerabilities.

Red Flags

Vendors that describe their AI red teaming as an extension of their existing application security testing without demonstrating AI-specific methodology may be repackaging conventional services. Those that cannot explain the difference between testing a traditional API and testing an LLM-powered endpoint likely lack the technical foundation for meaningful AI security assessment. Vendors that focus exclusively on automated scanning without manual, creative adversarial testing may miss the nuanced attack vectors that characterize AI systems. Proposals that do not mention prompt injection, training data risks, or output safety as distinct testing categories suggest insufficient understanding of the AI threat model.

Using This in Procurement

The guidance recommends incorporating these evaluation criteria directly into procurement processes for AI security services. Organizations should include AI-specific capability requirements in requests for proposals, ask vendors to demonstrate their methodology against a representative test system, and require evidence of AI security expertise among the proposed testing team. The document provides template evaluation scorecards that procurement teams can adapt to their specific requirements.

For organizations building internal red teaming capability rather than procuring external services, the criteria serve as a competency framework for hiring and training. The same dimensions used to evaluate vendors apply when assessing whether internal teams have the skills and tools needed for effective AI security testing.

Who This Resource Is For

Security leaders and CISOs evaluating AI red teaming vendors will find this the most directly useful guide available for making informed procurement decisions. Procurement teams handling security service contracts can use the evaluation criteria and scoring templates to standardize vendor assessment. AI engineering teams working with security partners can use the framework to communicate their testing needs more precisely. Internal red team leads building AI testing programs can use the criteria as a capability maturity model for their own teams.