AI red teaming coverage matrices expose the gap in agent security

At a glance

What this is: This is a framework for comparing AI red teaming vendors by testing breadth, depth, runtime validation, and reporting rather than relying on feature lists.

Why it matters: It matters because IAM, PAM, and NHI teams now have to judge whether security testing covers agentic behaviour, tool access, and control enforcement, not just model output.

By the numbers:

• The matrix covers 10 categories across four dimensions, including coverage depth, automation, runtime validation, and reporting.

👉 Read Akto's AI red teaming coverage matrix for vendor evaluation

Context

AI red teaming for agentic systems is no longer a single-model exercise. Once agents can read tickets, query databases, call APIs, and use connected tools, the question shifts from whether a model can be fooled to whether the surrounding identity, tool, and runtime controls can be pressured into unsafe action. This is why AI red teaming vendor evaluation now has to account for agentic AI security, MCP security, and runtime enforcement together.

The article argues that feature lists are not enough because breadth and depth can differ by an order of magnitude even when vendors use the same label. That framing is directly relevant to NHI governance: if the testing does not model tool use, permission boundaries, and control validation at runtime, practitioners are buying evidence of capability, not evidence of coverage.

Key questions

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

A: Use a coverage matrix that scores attack breadth, depth, runtime validation, and reporting. Focus on whether the platform tests the agent decision loop, connected tools, MCP paths, and multi-step abuse, not just model outputs. The right question is not whether the vendor does red teaming, but how much of the real attack surface it exercises.

Q: Why do feature lists fail to compare AI red teaming tools properly?

A: Because a feature list says only that a category is supported, not whether the platform tests shallow examples or realistic attack paths. In agentic environments, the difference between a few canned probes and multi-step takeover testing is material. Procurement teams need evidence of depth and runtime enforcement, not a checkbox.

Q: What breaks when AI red teaming ignores MCP security?

A: The tool broker becomes an untested privilege boundary, which means poisoned descriptions, confused-deputy paths, and over-broad permissions can slip through. Agents may then use legitimate access on an attacker’s behalf. Without MCP testing, the organisation can validate the model while leaving the access path exposed.

Q: How do compliance teams use AI red teaming evidence effectively?

A: Map findings to control language that auditors recognise, then show whether the control was validated at runtime. The useful evidence is not just that a weakness exists, but that it was tested against policy enforcement and action restrictions. That makes the result usable for governance, procurement, and review.

Technical breakdown

Coverage depth vs capability in AI red teaming

Coverage depth is the number of attack variants and paths a platform can test inside a given category, while capability is only the yes or no answer to whether a test exists. In AI security, that distinction matters because prompt injection, jailbreaks, MCP abuse, and retrieval poisoning are not single techniques but families of techniques. A product can claim support for prompt injection yet still miss indirect, multi-turn, or context-based variants. That creates a false sense of assurance, especially when the real risk is in the attack path the product never exercised.

Practical implication: evaluate the range of attack variants tested, not the marketing label attached to the category.

Why agentic AI security testing must inspect the decision loop

Agentic AI security testing examines what the agent does, not only what it says. That means the control target is the decision loop, where the system selects actions, tools, and sequences to complete a task. Traditional model red teaming mostly inspects outputs, but agentic risk emerges when a malicious prompt changes the goal, diverts the tool path, or causes unauthorised execution. This is a governance shift as much as a technical one, because the identity subject is now an acting system with a runtime path, not a passive responder.

Practical implication: require testing that observes task execution, tool invocation, and action sequence, not just generated text.

MCP security testing and runtime guardrail validation

Model Context Protocol servers sit between agents and the tools or data they can reach, so they become a control point for access, delegation, and confused-deputy failures. Security testing here has to probe poisoned tool descriptions, over-broad permissions, and cases where an agent is steered into using legitimate access on behalf of an attacker. Runtime guardrail validation matters because a policy that works on paper but not in execution is not a control. In practice, this is where NHI governance meets agentic behaviour: access brokered once is still access that must be constrained continuously.

Practical implication: test the broker layer and verify that guardrails block unsafe actions in production conditions.

Threat narrative

Attacker objective: The attacker wants to convert trusted agent access into unsafe tool use, data exposure, or downstream system compromise.

1. Entry occurs when a malicious prompt, poisoned document, or compromised tool context reaches the agent through connected systems and influences the task path.
2. Escalation follows when the agent uses legitimate tools or permissions in ways that exceed the intended scope, including indirect prompt injection, tool abuse, or MCP confusion.
3. Impact is achieved when the attacker uses the manipulated agent workflow to access sensitive data, trigger unsafe actions, or propagate abuse across connected systems.

Breaches seen in the wild

• Replit AI Tool Database Deletion — Replit vibe coding AI assistant deletes live production database and creates 4,000 fake user records.
• McDonald's McHire AI Chatbot Default Credentials — Default credentials in McDonald's McHire AI recruitment chatbot expose 64 million job application records.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Coverage matrices are becoming the only credible way to separate agent security from model theater. A checkbox that says prompt injection does not tell a practitioner whether the vendor tests direct, indirect, and multi-turn variants, or whether it validates those findings against runtime controls. That gap matters because agentic systems fail at the interaction layer, not just the model layer. Practitioners should treat breadth and depth as the real buying criteria.

Agentic AI has collapsed the old assumption that model testing is sufficient to understand identity risk. The assumption that a model can be reviewed in isolation was designed for systems that answered questions, not systems that choose tools and execute tasks. That assumption fails when the actor is autonomous because the relevant harm emerges in the decision loop, tool path, and execution timing. The implication is that security teams must rethink how they define test coverage, not merely add more tests.

MCP security is the new control boundary for many AI deployments. Once agents broker access through tool servers, permission problems stop being abstract model issues and become concrete delegation issues. A testing program that ignores the broker layer misses where privilege is actually translated into action. Practitioners should evaluate MCP exposure as part of identity governance, not as a separate AI feature check.

Runtime validation turns red teaming from a report into an operating control. Discovery without enforcement only proves that an attack exists. The article’s core insight is that control effectiveness has to be proven where the agent acts, because static assessments cannot guarantee that guardrails still hold when prompts, tools, and context all change. Security teams should use this as a benchmark for whether a programme is measuring risk or merely documenting it.

AI red teaming is converging with NHI governance because agent identity and tool access now move together. The more an agent can read, call, and chain tools, the more the organisation must ask whether access is bounded, auditable, and continuously validated. That makes identity governance, runtime control, and compliance evidence part of the same operating model. Practitioners should plan for a single control conversation across agentic AI, NHI, and IAM.

From our research:

• 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate, according to AI Agents: The New Attack Surface report.
• Only 44% have implemented policies to govern AI agents, leaving most organisations with threat awareness but limited control coverage.
• That governance gap is why OWASP Agentic AI Top 10 matters as a design baseline for testing, control validation, and runtime safeguards.

What this signals

Coverage depth is becoming the procurement differentiator for AI security teams. As agents move from chat to action, shallow tests no longer tell you whether your programme can absorb indirect prompt injection, tool abuse, or multi-agent escalation. Teams that already use OWASP NHI Top 10 as a control lens should extend that thinking into red-team selection, because the testing surface now mirrors the operating surface.

Runtime validation will separate evidence from theatre in 2026 procurement cycles. Organisations increasingly need to prove that guardrails work after deployment, not merely that a lab test found a flaw. The practical signal is whether your programme can demonstrate blocked actions, preserved auditability, and clear control mapping when agent behaviour changes in production.

MCP and agent discovery are becoming governance prerequisites, not nice-to-have features. If you cannot inventory the agent, the tool broker, and the delegated permissions, you cannot test them with confidence. That is the same accountability problem seen in broader NHI governance, and it is where mature teams should start aligning their identity, security, and compliance workstreams.

For practitioners

• Define coverage by attack family and variant depth Build vendor scorecards around direct, indirect, and multi-turn prompt injection, plus jailbreak, tool abuse, RAG poisoning, and multi-agent escalation. A yes or no answer is not enough when coverage depth varies by an order of magnitude.
• Test the MCP broker layer explicitly Ask vendors to demonstrate poisoned tool descriptions, confused-deputy cases, and permission failures in MCP-connected paths. If the broker layer is not tested, agent access can be abused through legitimate delegation.
• Require runtime validation, not static findings Verify that the platform proves guardrails block unsafe actions in live or production-like conditions. Findings without enforcement evidence only document risk; they do not prove control effectiveness.
• Score reporting for audit usefulness Demand output that maps attacks to controls, severity, and evidence in a form compliance, legal, and security teams can all use. If the report cannot support third-party scrutiny, it is not mature enough for procurement.

Key takeaways

• AI red teaming for agents must be judged by coverage depth, runtime validation, and broker-layer testing, not by a feature checklist.
• The core risk is not only model abuse but the path from agent decision to tool action, which is where identity and access assumptions fail.
• Practitioners should align red teaming, MCP governance, and runtime guardrails so testing proves control effectiveness in the environment where agents actually operate.

Key terms

• AI Red Team Coverage Matrix: A structured comparison tool that scores AI security vendors on what they test, how deeply they test it, and whether results are validated at runtime. It matters because two products can claim the same capability while covering very different attack surfaces.
• Agentic AI Security: Agentic AI security is the discipline of securing autonomous AI systems that can take actions, use tools, and chain decisions without direct human approval at each step. It covers identity and access management for AI agents, prompt injection defence, tool call governance, credential scoping, and runtime monitoring. As agentic systems acquire real-world authority — API access, file writes, workflow triggers — the security model must treat them as non-human identities with explicit lifecycle controls, not trusted processes.
• MCP Security: MCP security is the set of controls that protect Model Context Protocol connections between agents, tools, and data sources. It covers connector permissions, secret handling, and policy enforcement because the protocol can become a direct path from agent intent to enterprise action.
• Guardrail Validation: Guardrail validation is the process of proving that an AI control actually blocks, redirects, or records the behaviour it is meant to govern. For agentic systems, validation must include adversarial prompts, tool calls, and regression testing so the control is shown to work at runtime, not just in design.

What's in the full article

Akto's full post covers the operational detail this post intentionally leaves for the source:

• A vendor-by-vendor coverage matrix template you can reuse in procurement and internal evaluation.
• A 12-question assessment list for comparing AI red teaming platforms across attack categories and runtime validation.
• Detailed scoring guidance for prompt injection, agentic testing, MCP security, RAG poisoning, and multi-agent abuse.
• Examples of how reporting maps test findings to compliance and audit evidence.

👉 Akto's full post breaks down the matrix template, scoring model, and vendor questions in implementation detail.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.