AI agent evaluations versus attacks: are your controls enough?

TL;DR: AI agent evaluations can show expected behaviour, but live attacks reveal how tools, prompts, and runtime access combine to create failure paths that tests miss, according to ZioSec. The real issue is that agent governance is still being treated like static software testing when it needs identity-aware attack validation.

NHIMG editorial — based on content published by ZioSec: AI Agents: Evaluations Versus Attacks

Questions worth separating out

Q: How should security teams test AI agents beyond standard evaluations?

A: Security teams should combine evaluations with adversarial attack testing that manipulates prompts, tool calls, and runtime context.

Q: Why do AI agent attacks reveal more risk than evaluations alone?

A: Because attacks simulate hostile conditions that evaluations usually exclude.

Q: What do security teams get wrong about AI agent governance?

A: They often separate model testing from identity governance, even though the two failure modes are linked.

Practitioner guidance

• Run adversarial tests on agent identity paths Test how prompts, tool requests, and external data sources change agent behaviour under attack.
• Inventory every tool and credential an agent can reach Map each AI agent to the exact APIs, databases, and secrets it can access at runtime.
• Treat evaluations as one control, not the control Use evaluations to measure intended behaviour, then pair them with live attack campaigns that probe for privilege misuse, prompt injection, and unintended action chaining.

What's in the full article

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

• How the team structures live attack campaigns against AI agents and what inputs they target.
• Examples of the kinds of agent failure patterns developers should look for during security review.
• The operational difference between evaluating model behaviour and testing runtime abuse paths.
• How a security architect frames offensive testing for teams building agentic workflows.

👉 Read ZioSec's analysis of AI agent evaluations versus attacks →

AI agent evaluations versus attacks: are your controls enough?

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