Before connecting an autonomous IT agent, verify what it is meant to do, what identities and systems it can reach, and what stops an unsafe action from running. Evaluate the complete agent-and-tools system—not just the model’s answers—and begin with the narrowest access that can support the intended task.
1. Define the task and the harm boundary
Write down the agent’s intended tasks, the records and systems those tasks require, and the worst credible outcome if the agent makes a mistake or is manipulated. Distinguish reading information from changing access, configuration, records, money, or communications visible outside your organization. The system owner and risk owner should agree on what is in scope before testing begins.
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This boundary gives you a practical test: can the agent complete the approved task without receiving broader access or causing effects the task does not require?
2. Map identity, permissions, tools, and dependencies
Trace the full path from the agent’s identity through authentication and authorization to its tools, APIs, data sources, and downstream systems. Ask the supplier or internal team for a current access map, not just a list of model capabilities.
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- What identity does the agent use, and how does it authenticate?
- Are identities shared across users, tasks, or environments? If so, what does that mean for attribution and limiting access?
- What permissions and scopes are granted, and can they be reduced to those required for the intended task?
- Which tools and APIs can the agent invoke, and what can each one read or change?
- How are credentials stored, rotated, and revoked?
- What connected services or downstream systems could be affected by an action?
NIST’s NCCoE agent identity and authorization project highlights identity, authorization, and governance as developing concerns for systems that take autonomous actions. Its resource hub notes that traditional identity approaches may not fully address these challenges: NIST NCCoE Agentic AI Identity and Authorization Project Resource Hub.
3. Test realistic hijacking and misuse
Test with the kinds of content the production agent will actually read. Untrusted text in an email, file, or web page can contain instructions that try to redirect the agent; NIST describes this as agent hijacking. Include scenarios that test both the agent’s behavior and whether the surrounding controls prevent harmful effects.
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- Embed a malicious instruction in an otherwise ordinary email, document, or page the agent is asked to process.
- Ask the agent, through untrusted content, to disclose data to an unauthorized destination.
- Try to induce a tool call that falls outside the user’s task or the agent’s approved scope.
- Test multi-step actions that exceed the user’s intended goal, including attempts to chain individually permitted operations into a harmful outcome.
Record outcomes by task and attack category, including whether sensitive data was exposed, an unauthorized tool was invoked, or an external change occurred. Repeat the tests after material changes to the model, prompts, tools, permissions, or connected data. NIST’s 2025 CAISI evaluation used simulated Workspace, Travel, Slack, and Banking contexts, and added risk areas including database exfiltration and automated phishing. In one experiment on an upgraded Claude 3.5 Sonnet agent configuration using held-out Workspace tasks, the strongest baseline attack had an 11% measured success rate, while the strongest new attack had an 81% rate. Those are results for that experimental setup, not an expected failure rate for deployed agents. NIST’s point is that tests must adapt as attackers target a system’s specific weaknesses: NIST CAISI: Strengthening AI Agent Hijacking Evaluations.
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The model’s decision to call a tool is not an authorization decision. Inspect the component that executes tool calls and confirm it independently checks the actor’s authority, the operation’s scope, and any required approval before the action runs. The agent must not be able to grant itself broader access by asking for it in natural language.
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For high-impact actions, check whether approval is bound to the actual operation: the actor, tool, target, normalized parameters, timestamp, and expiry. Confirm that execution fails closed if policy, approval, or audit checks are unavailable or fail. OWASP recommends separating decision-making from execution and validating these controls at the execution boundary: OWASP AI Agent Security Cheat Sheet.
5. Inspect output handling, isolation, and operational evidence
Review how the system handles data and code before they can produce effects. Outputs should be validated before execution or display, sensitive-data leakage should be considered, and tool scope and rate limits should constrain possible impact. If the agent can run code, verify that execution is isolated; OWASP warns against unrestricted tool access and arbitrary code execution without sandboxing.
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Ask to see execution and policy records that show what action was attempted, under which identity and authority, what checks ran, and what result followed. An agent’s explanation of its own behavior is not a substitute for those records.
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If you are evaluating more than one agent, run the same representative tasks and attack cases against each candidate. Compare evidence on the dimensions below rather than relying on a general security claim or a model-only benchmark. This is a practical comparison framework drawn from NIST and OWASP guidance, not a published vendor ranking or universal scoring standard.
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| Comparison dimension | What to verify |
|---|---|
| Identity and permission granularity | Whether access can be limited to the required identity, task, data, and operation. |
| Reachable tools and impact | Which tools and downstream systems are available, and what each can change or disclose. |
| Execution-time authorization | Whether a separate execution component validates authority, scope, and approval for the exact operation. |
| Resistance to hijacking and exfiltration | Results on the same task-specific prompt-injection, unauthorized-disclosure, and out-of-scope-action tests. |
| Approval and audit evidence | Whether approvals apply to the exact action and records allow reviewers to reconstruct what happened. |
| Isolation and output validation | Whether code and outputs are constrained and checked before they can affect systems or users. |
| Operational control | Whether access can be restricted, monitored, and revoked in the environment where the agent will run. |
7. Approve only the tested scope, and reassess on change
Document unresolved risks, required mitigations, and the person accountable for accepting them. Authorize only the access scope covered by your evaluation; treat any expansion as a new risk decision rather than assuming that success on a narrower scope carries over.
Set reassessment triggers for material changes to the model, prompts, tools, permissions, connected systems, or threat conditions. NIST CAISI’s evaluation work underscores why an old passing result cannot establish resistance to newly tailored attacks: NIST CAISI agent hijacking evaluation.
How to use current NIST guidance
NIST’s AI Risk Management Framework (AI RMF) 1.0 is voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation; NIST says the framework is being revised. It can inform risk-management decisions, but it is not an agent-specific security certification: NIST AI Risk Management Framework.
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NIST NCCoE’s agent identity and authorization work is an active project. Its resource hub describes an eventual SP 1800-series practice guide, so treat that guide as planned rather than already published. The hub reports more than 600 responses to its February 2026 concept paper; that is a participation count, not evidence of security performance: NIST NCCoE Agentic AI Identity and Authorization Project Resource Hub.
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