How should security teams evaluate AI red teaming vendors for agentic systems?

Why This Matters for Security Teams

ai red teaming for agentic systems is not a model-quality exercise. Security teams need vendors that can probe the full decision loop: prompt handling, tool selection, memory, external calls, and the paths that connect an agent to data, code, and credentials. The same gap shows up in incidents like the Gemini AI Breach — Google Calendar Prompt Injection and CoPhish OAuth Token Theft via Copilot Studio, where abuse moves through integrated services rather than through the base model alone.

The evaluation problem is getting harder because agent risk is already mainstream. In SailPoint’s AI Agents: The New Attack Surface report, 80% of organisations said their agents had already acted beyond intended scope. That is exactly why vendors must show evidence of runtime abuse testing, not just static prompt fuzzing. Current guidance from the OWASP Agentic AI Top 10 and NIST AI Risk Management Framework points toward broader system-level evaluation, but there is no universal standard for vendor scoring yet.

In practice, many security teams discover that a vendor tested the demo prompt, not the agent’s real blast radius, only after a tool chain or MCP path has already been abused.

How It Works in Practice

A useful vendor review starts with a coverage matrix. The strongest candidates can map tests to the agent’s actual attack surface: the model, system prompt, planner, memory, tool connectors, permission boundaries, and any Analysis of Claude Code Security-style developer workflows. That matters because agentic abuse is usually multi-step. A single malicious instruction may only become dangerous after the agent has chained tools, fetched context, or reused a token in a later step.

Security teams should ask vendors how they simulate the attack lifecycle, not just the initial injection point. A credible platform should explain whether it tests:

• Prompt injection, indirect prompt injection, and instruction hierarchy conflicts
• Unauthorized tool invocation, including retries and chained actions
• Credential exposure, token theft, and misuse of long-lived secrets
• MCP routes, API gateways, and other integration paths
• Multi-turn abuse where the agent is steered across several decisions

That evaluation should also include runtime validation. A finding is more valuable if the vendor can prove whether the agent actually executed the action, what guardrail fired, and whether the control prevented data access, side effects, or privilege escalation. This is where the CSA MAESTRO agentic AI threat modeling framework and MITRE-style attack mapping help teams compare vendors on realistic execution paths instead of marketing claims. The right vendor also reports findings in a way that separates model weakness from orchestration weakness, because remediation differs. These controls tend to break down when the agent has broad tool access across SaaS, internal APIs, and privileged workflows because the vendor cannot safely replicate the real enterprise context.

Common Variations and Edge Cases

Tighter testing often increases time, integration effort, and the chance of operational friction, so organisations have to balance depth against speed and budget. That tradeoff is especially visible when vendors want full access to production-like connectors or real credential scopes. Best practice is evolving, but the current consensus is that synthetic test environments alone are not enough for agentic systems.

Some vendors focus on red teaming the base model because it is easier to package and benchmark. That can still be useful, but it does not answer the enterprise question if the agent depends on external memory, third-party SaaS, or workflow automation. Security teams should treat claims of “agent testing” cautiously unless the vendor can demonstrate coverage of autonomous actions, tool misuse, and post-compromise behaviour. A strong signal is whether the vendor can explain how it handles Replit AI Tool Database Deletion-style failures, where the real issue is execution authority rather than output quality.

For regulated or high-privilege environments, the evaluation should also ask whether the vendor can test identity and session misuse, not only content safety. Agent red teaming becomes less meaningful when the platform cannot model short-lived secrets, delegated access, or the exact trust boundaries that matter in production. In those environments, a shallow red team can look comprehensive while missing the paths that matter most.

Related resources from NHI Mgmt Group

• How should security teams govern machine identity credentials in agentic AI environments?
• How should security teams limit the risk from AI agents that have access to production systems?
• How should security teams govern AI agents that can access enterprise systems?
• How should security teams run AI red teaming for GenAI systems?