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Before giving an autonomous IT agent access, define what it is allowed to do, map every identity, data source and tool it can reach, and test whether it can be manipulated into taking an unauthorized action. Most importantly, verify that a separate execution layer—not the agent’s own reasoning—enforces permissions, approvals and logging. Begin with the narrowest tested access scope and expand only when the evidence supports it.
What should you evaluate before connecting an agent?
Evaluate the complete system that can act: the agent, its identity and credentials, its instructions, the data it reads, its tools and APIs, and the services those tools can affect. A model’s fluent or reassuring response is not proof that its actions are safe. The execution path determines what the agent can actually do.
Use the same representative tasks and adversarial cases to assess every candidate. The criteria below are a practical synthesis of NIST’s agent-hijacking evaluation work and the OWASP AI Agent Security Cheat Sheet; they are not a certification or guarantee of safety.
1. Define the task and the harm boundary
Write down the intended jobs before discussing access. Describe what the agent should accomplish, which records and systems those tasks require, and what the worst credible result would be if it misunderstood a request or followed hostile content. Have the system owner and risk owner agree on the boundary.
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Separate observation from action. Reading a ticket, summarizing a document or searching a knowledge base has a different consequence profile from changing permissions, editing production configuration, sending a message externally or initiating a transaction. Identify actions that must never be autonomous and actions that require human approval.
- Specify permitted tasks and examples of out-of-scope requests.
- Name affected systems, data classes, users and external destinations.
- Identify impacts such as data exposure, service disruption, unauthorized changes or misleading communications.
- Define what counts as an acceptable result, a blocked action and a test failure.
2. Map identity, permissions and reachable systems
Ask the supplier or internal team for an end-to-end access map. Trace the agent’s identity through authentication, tools and APIs to each downstream system. Include indirect access: a seemingly limited tool may be able to trigger a workflow with broader consequences.
NIST’s NCCoE Agentic AI Identity and Authorization project focuses on identity and authorization practices for agents and notes that traditional identity approaches may not fully address emerging agent challenges. Treat identity as a deployment-specific design question rather than assuming that a familiar account or service principal settles it.
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- Identity: Is there a distinct identity for the agent, and is it shared across users, sessions or tasks? How is it authenticated?
- Permissions: What scopes, roles and data permissions are granted? Can they be reduced to the specific task and environment?
- Credentials: Where are secrets stored, who can access them, how are they rotated, and how quickly can they be revoked?
- Tools and dependencies: Which connectors, APIs, scripts, databases and downstream workflows are reachable, including through other tools?
- Limits: Are rate, record, destination and action limits enforceable outside the model?
3. Test prompt injection and misuse with realistic content
Untrusted material can contain instructions that attempt to redirect an agent. NIST describes agent hijacking as malicious instructions embedded in otherwise ordinary task data, including email, files and websites. Test the kinds of content your deployment will actually ingest—not only clean prompts written for a demonstration.
Use an isolated test environment with representative permissions and synthetic or otherwise approved data. Include cases such as:
- An email, document or web page tells the agent to ignore its task and disclose information.
- Content asks it to send sensitive data to a destination not authorized for the user or task.
- A request tries to invoke a tool outside the intended workflow or to expand access through natural-language instructions.
- A sequence of individually plausible steps accumulates into an action beyond the user’s intended goal.
- A tool response or retrieved record contains hostile instructions that conflict with the task.
Record outcomes by attack category and task: whether the agent attempted the action, whether the execution layer blocked it, what data or system was exposed, and whether the event was logged. A blocked attempt is useful evidence about the enforcement boundary; an agent’s verbal refusal alone is not. Repeat tests after material changes to the model, prompts, tools, permissions or connected data.
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NIST’s 2025 evaluation used AgentDojo’s simulated Workspace, Travel, Slack and Banking contexts and added risk areas including database exfiltration and automated phishing. In a held-out Workspace experiment on an upgraded Claude 3.5 Sonnet agent configuration, NIST measured attack success of 11% for the strongest baseline attack and 81% for the strongest new attack. Those figures describe that specific experiment, not the expected failure rate of an agent in production. The practical lesson is to adapt attacks to the system being evaluated rather than treating a pass against old cases as proof against new ones. NIST CAISI evaluation details.
4. Verify that controls are enforced outside the agent
Inspect the component that actually executes tool calls: for example, a policy service, gateway or application layer. It should independently check the acting identity, requested operation, target, scope and approval state before allowing an action. The agent must not be able to authorize itself simply by asking for broader access in natural language.
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For consequential actions, verify that approval is specific to the operation being approved. OWASP recommends separating decision-making from execution and binding approval to details such as the actor, tool, target, normalized parameters, timestamp and expiry. Check that changing a target or parameter invalidates the approval, and that the system fails closed if policy, approval or audit checks are unavailable. See the OWASP guidance.
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- Attempt an out-of-scope action and confirm that the execution layer rejects it.
- Change a parameter after approval and verify that the action requires a fresh approval.
- Test what happens when the policy service, approval check or audit system is unavailable.
- Confirm that a user cannot gain authority merely because the agent can reach a tool.
5. Inspect output handling, isolation and audit evidence
Controls should cover what comes out of the model as well as what goes into it. Verify that outputs are validated before execution or display, especially when they contain tool parameters, code, commands, destinations or user-facing claims. Consider data leakage in both tool calls and generated responses.
Check whether code execution is isolated and whether tool access is restricted to the required operations. OWASP warns against unrestricted tool access and arbitrary code execution without sandboxing. Review rate limits and other constraints that cap the possible effects of a mistake.
Ask to inspect execution and policy records, not just the agent’s explanation of what it did. Logs should let an authorized reviewer reconstruct the relevant action and context, including the actor, tool, target, parameters, authorization or approval result, and outcome. Determine who can review the records and how the organization detects and responds to suspicious activity.
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6. Compare agents using the same evidence
There is no universal score in the cited guidance that establishes an agent as safe. Use a common set of tasks, permissions and attack cases, then compare evidence against the deployment’s own risk boundary. A stronger model response is not a substitute for narrow permissions or independently enforced controls.
| Evaluation area | Evidence to request or test | What a sound result looks like |
|---|---|---|
| Identity and permission granularity | Identity and credential design; effective scopes; ability to restrict and revoke | Access is attributable, limited to the task, and revocable without relying on the agent |
| Reachable tools and impact | Tool, API and downstream-system inventory; representative action tests | Every reachable action has an understood owner, purpose and consequence |
| Execution authorization | Policy checks, approval binding and denial tests | Authorization is independently enforced for the exact operation and target |
| Hijacking and misuse resistance | Results for representative prompt-injection, exfiltration and out-of-scope cases | Attempts are measured by category; unauthorized actions are blocked and recorded |
| Output validation and isolation | Validation rules, sandbox design, tool limits and rate controls | Unsafe outputs cannot directly produce unbounded or unintended effects |
| Audit and operations | Sample logs; monitoring, revocation and incident-response procedures | Reviewers can determine what happened and operators can contain access promptly |
7. Make a scoped decision and set reassessment triggers
Record the tested configuration, unresolved risks, required mitigations and the person accountable for accepting residual risk. Approve only the access and actions covered by the evaluation. If the proposed deployment changes the model, prompts, tools, connected systems or permissions, it is a changed system—not automatically the same test result.
Set reassessment triggers for those material changes and for meaningful changes in threat conditions. NIST CAISI notes that evaluations need to adapt as attacks are developed against the system under test; this is a practical governance recommendation, not a universal NIST requirement.
Which guidance can help structure the review?
NIST describes its AI Risk Management Framework 1.0 as voluntary guidance for incorporating trustworthiness considerations into AI design, development, use and evaluation. NIST’s overview says the framework is being revised, so check its current status rather than treating it as a fixed agent-specific standard.
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The NCCoE agent identity resource hub describes an active project working toward implementation-oriented materials, with an eventual SP-1800 series practice guide. Do not treat that future guide as already published; consult the project resource hub for current materials and updates.
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