Picking an AI red teaming vendor is getting harder
Vendor noise is already a problem in traditional security testing. AI red teaming has added another layer of confusion, with providers offering everything from consulting engagements to automated testing platforms. Many buyers still struggle to tell whether a vendor can test real-world AI system behavior or only run a packaged set of jailbreak prompts.
This problem is addressed directly in OWASP’s Vendor Evaluation Criteria for AI Red Teaming Providers & Tooling, a practical guide for evaluating AI red teaming service firms and automated tools across both basic GenAI deployments and advanced agentic systems. The guide is written for buyers who need to make decisions under pressure, including CISOs, security architects, governance teams, and procurement leaders.
Simple GenAI systems still carry serious risk
Most enterprise deployments still fall into the “simple system” category. That includes customer-facing chatbots, internal copilots for HR or IT, workflow assistants, and retrieval-augmented generation (RAG) systems connected to internal documents.
These systems tend to fail in familiar ways. Jailbreaks remain common. Prompt injection continues to work in many environments. Hallucinations create business risk when employees treat outputs as trusted answers. Sensitive data leakage shows up through conversation history, retrieval chains, or weak access controls.
The evaluation criteria emphasize that vendors should be able to test these risks through multi-turn adversarial conversations, persona-based manipulation, and RAG-specific attacks such as retrieval override and semantic hijacking. Vendors should also be able to stress-test safety behavior under repeated attempts, since many models behave differently across sessions.
Advanced systems require different testing skills
A growing number of organizations are deploying AI systems that take action, not just generate text. These include tool-calling agents, MCP-based architectures, and multi-agent workflows that coordinate tasks across different components.
The guide treats these systems as a separate class because the risk surface expands quickly. Tool calling introduces schema manipulation risks. MCP tool registries create capability exposure issues. Multi-agent systems introduce message-passing vulnerabilities and cross-agent contamination. Persistent memory creates opportunities for poisoning and instruction planting.
The criteria also call out privilege escalation pathways, including situations where a user-level agent can be manipulated into invoking admin-level tools. Stateful testing becomes a baseline requirement, since many of these systems store context across sessions or pass memory between agents.
Green flags and red flags help filter vendors fast
The guide includes an executive screening section that reads like a quick triage checklist.
Strong vendors show reproducible multi-turn evaluations, custom testing that produces novel findings, and reporting that maps technical failures to business impact. Human verification is treated as a core expectation for high-severity findings. Testing capability across stateful systems is another major credibility signal.
Weak vendors show predictable patterns. Stock jailbreak libraries passed off as original work are a major warning sign. Another is vague claims of one-click testing or complete coverage. Vendors who focus only on model output, without testing tool calls or workflow actions, fail to evaluate the risks that matter most in agentic deployments.
Metrics matter more than marketing scores
Many AI security products rely on scoring systems that sound scientific and repeatable. The guide pushes buyers to demand measurable, transparent metrics tied to real risk.
For simple systems, examples include jailbreak success rate, guardrail bypass rate, hallucination frequency, and leakage severity. For RAG deployments, metrics should measure retrieval reliability under adversarial load.
For advanced systems, metrics need to capture tool misfire frequency, unsafe tool-call rates, MCP capability misuse coverage, and multi-agent contamination rates.
The guide also warns against opaque scoring systems that do not explain how severity is calculated or how results can be reproduced. It treats reproducibility as essential, especially when non-deterministic model behavior makes results inconsistent across runs.
Operational fit is part of security testing
The evaluation criteria also focus on practical integration issues. Serious vendors should support CI/CD integration for regression testing, safe sandboxing for destructive tool calls, and logging that enables deterministic replay of multi-step attacks.
Data governance is treated as a key part of vendor evaluation. Buyers should expect specific answers on retention policies, isolation of sensitive prompts and outputs, access controls, and whether third-party model providers receive customer data. On-prem and self-hosted options are presented as important for sensitive environments.