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Evaluate the complete AI agent system—not just its model—by giving it realistic user tasks in an isolated environment where tool-returned emails, webpages, or files contain adversarial instructions. Measure whether it completes the legitimate task, resists the injected instruction, keeps data within its authorized boundary, and obtains approval before consequential actions. Repeat the tests, record the system configuration and potential impact, and pair testing with narrow permissions and bounded tools. A good result is evidence about the setup and attacks tested, not proof that the system is universally safe.
What a tool-access safety evaluation needs to establish
Tool access changes what an AI system can do. An agent that can read a message, browse a page, or inspect a file may also be able to send messages, alter records, execute code, or pass data to another service. The safety question is therefore not simply whether the model can identify suspicious text. It is whether the deployed system can carry out a user’s task without treating hostile content returned by a tool as authority to take a different action.
NIST Center for AI Standards and Innovation (CAISI) calls this kind of attack agent hijacking: an attacker places instructions in data the agent may ingest in an attempt to override the user’s request. A representative evaluation pairs a legitimate task with a malicious instruction embedded in external material, then records whether the agent attempts or completes the malicious task. The test should cover both the agent’s decision and what its tools, permissions, and safeguards allow it to do.
For example, a normal task might ask an agent to summarize a document or find a meeting time. A file or webpage it reads could contain a second instruction to disclose private information, send a message, or modify a record. The test should distinguish the agent noticing that instruction from acting on it. It should also distinguish an attempted action from a completed one: a permission boundary may block an unsafe attempt, but that boundary does not mean the agent behaved correctly.
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Safety is contextual. An agent with read-only access to public material presents a different risk from one that can access confidential records and send external messages. Define what “safe enough” means for the intended use, data, tools, and possible harms before running tests. No reviewed source establishes a universal benchmark score or certification threshold for safe tool access.
Test the deployed system, not a model score in isolation
The system under evaluation includes the model, its instructions, tool definitions, permissions, approval gates, connectors, and runtime safeguards. A model-only result cannot tell you exactly how a configured agent will behave: tools can expand its capabilities, while permission checks or approval gates can prevent a harmful operation. Conversely, a narrow model evaluation may omit risks introduced by the real tool environment.
Keep model-behavior results and full-system results separate in reports. OpenAI’s ChatGPT Agent System Card makes this distinction explicitly: its reported test of model behavior does not represent the complete end-to-end prompt-injection mitigation stack. A system evaluation should identify which safeguards were active and whether they actually blocked, escalated, or allowed the tested action.
When comparing configurations, keep the task distribution and scoring axes consistent. Otherwise, a higher success rate could reflect easier tasks or a narrower attack set rather than a safer system. Compare normal-task completion alongside attack outcomes; an agent that refuses every task may avoid some attacks but is not functioning as intended.
A practical evaluation plan
1. Define the system boundary and possible harms
Inventory the complete path from user request to tool action. Record what the agent can read, what it can change, which identities it uses, and what systems each connector can reach. Include actions that appear low risk individually but could expose sensitive information or enable a later harmful action.
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- Confidentiality: Could the agent reveal data to an unauthorized person or destination?
- Integrity: Could it create, edit, send, or delete something the user did not authorize?
- Availability: Could it disrupt access or operations, for example by changing or removing records?
- Authority: Which actions require user approval, and how is that approval obtained?
- Scope: Which accounts, files, records, or services are available to each tool?
This boundary matters because excessive agency can come from excessive functionality, excessive permissions, or excessive autonomy. OWASP’s LLM06:2025 Excessive Agency recommends minimizing available extensions, exposed functions, and their permissions. Use this inventory to identify what an injected instruction could realistically cause—not merely what wording it might contain.
2. Pair realistic user tasks with injected instructions
Build scenarios around the workflows the system is meant to perform. Put adversarial text in realistic tool-returned content, such as an email, webpage, or file, rather than testing only direct instructions typed by the user. The conflict should be clear in the test design: the user has authorized one task, while untrusted content attempts to redirect the agent.
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Include benign tasks as well as attacks. A test should show whether the agent can complete normal work while rejecting or safely handling hostile content, not just whether it can detect an injection when the only objective is to refuse.
3. Isolate the evaluation and use controlled data
Run the evaluation in a sandbox separated from production accounts, live customer data, and real external recipients. Use controlled test data and simulated services where possible. The point is to observe behavior without allowing a successful attack to cause real-world damage.
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AgentDojo, the framework used in NIST CAISI’s experiments, includes simulated Workspace, Travel, Slack, and Banking environments. These are examples of how realistic workflows can be tested without using the corresponding live systems. Choose a test environment that represents the agent’s relevant tools and actions; a sandbox that omits a consequential connector cannot establish how the agent will behave with that connector enabled.
4. Repeat attempts and adapt the attacks
Do not treat one successful defense as a stable result. Agent behavior can vary across attempts, so run each scenario multiple times and report the number of attempts. Test known attack patterns, then have red-teamers adapt instructions to the particular system, its tools, and its normal workflow. NIST CAISI recommends adaptive evaluations, task-specific analysis, and testing across multiple attempts.
NIST CAISI technical staff wrote on January 17, 2025: “Evaluations need to be adaptive. Even as new systems address previously known attacks, red teaming can reveal other weaknesses.” In practice, this means a fixed test set is useful for tracking regressions, but it should not be treated as the full attack surface. Maintain stable scenarios for comparison and add new system-specific scenarios as weaknesses or changed capabilities emerge.
5. Score task completion, hijacking, and impact separately
Record outcomes at the task level. At a minimum, capture whether the legitimate task was completed, whether the injected instruction was attempted, whether it was completed, whether data crossed its authorized boundary, and whether the agent sought approval before a consequential action. Note the impact if the attempted action had succeeded.
Keep attempted actions distinct from completed actions. An unsafe attempt blocked by a permission gate is different from an operation that succeeds, but both are useful signals: the first tests the agent’s behavior and the gate’s effectiveness; the second demonstrates a failure with realized impact. Record which safeguard stopped an action rather than attributing every blocked outcome to the model.
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Do not rely on a single average. A high aggregate score can conceal a rare but severe outcome, and tasks can have very different consequences. Report both the aggregate and scenario-by-scenario results, with potential impact. An agent that resists most low-impact attacks but once sends confidential data may require a different decision from one with the same average that fails only harmless tasks.
6. Retest when the system changes
Re-run relevant scenarios after changes to the model, prompts, tool definitions, permissions, connectors, or safeguards. A change that appears unrelated to an attack can alter what the agent can access or how its actions are constrained. Keep the scenario set evolving to account for new capabilities and adaptive attacks; retain a stable core so results can still be compared over time.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What published test results do—and do not—tell you
Published figures are useful as examples of why context and task-level reporting matter. They are not interchangeable benchmarks: the systems, tasks, and measurements differ.
| Result | What was tested | How to interpret it |
|---|---|---|
| 11% baseline attack success; 81% for the strongest novel attack | NIST CAISI’s 2025 Workspace red-team experiment against an upgraded Claude 3.5 Sonnet agent, using attacks developed for that system and held-out user tasks. | These are results for that particular experiment and setup, not a general attack-success rate for AI agents. |
| 57% average success | The average across five specific injection tasks NIST used to illustrate the importance of task-level results. | The tasks had different potential impacts, so the average alone did not show the risk of each failure. |
| 99.5% success in disregarding irrelevant instructions | OpenAI’s synthetic text-browser challenge set, reported in the ChatGPT Agent System Card; the page reviewed did not state a publication date. | This is one test of model behavior. OpenAI notes that it does not represent the complete end-to-end prompt-injection mitigation stack. |
Do not compare these percentages as if they were scores from the same benchmark. NIST’s figures concern particular Workspace red-team and injection-task experiments; the OpenAI figure concerns a synthetic text-browser challenge set. Each result needs its test setup and metric to be meaningful. None supplies a universal pass score for your deployment.
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Controls to pair with testing
Evaluation reveals failure modes; it does not replace design controls. OWASP’s guidance on excessive agency recommends limiting the extensions available, the functions each extension exposes, and the permissions attached to them. Prefer a narrowly scoped function—such as writing a specific file—over an open-ended shell tool when the narrower capability meets the task. Use the user’s identity and the minimum privileges required for downstream access.
Require independent approval for high-impact operations, such as deleting documents. Approval should be a meaningful checkpoint for the consequential action, not a blanket authorization that silently covers unrelated actions. Test whether the agent requests approval in the scenarios where it should, and whether the operation remains blocked until approval is granted.
OpenAI’s API guidance, Safety in building agents, recommends keeping tool approvals enabled, using structured outputs to constrain what flows between steps, applying guardrails, and grading traces and evaluations. It also warns that these measures reduce risk but do not eliminate mistakes or prompt-injection susceptibility. Treat them as layers to test, not as guarantees that make adversarial evaluation unnecessary.
The same guidance says Agent Builder is being deprecated and is scheduled to shut down on November 30, 2026. Its interface directions should not be treated as timeless setup instructions. The underlying practices—approval, constrained data flow, guardrails, and evaluation—are the relevant points here.
What to include in an evaluation report
A useful report lets another reader understand what was tested and what a passing result means. Include enough detail to reproduce the test or recognize where its conclusions stop.
- System configuration: model and version, prompts or relevant instruction setup, tools and connectors, permissions, approval gates, and active safeguards.
- Environment: sandbox or simulation used, test data, identity and access scope, and any important difference from production.
- Scenario set: normal tasks, injected content and attack goals, and which system capabilities each scenario exercised.
- Execution: evaluation date, number of attempts per scenario, and whether attacks were fixed, adapted, or both.
- Outcomes: legitimate task completion, attempted and completed hijacking, data-boundary violations, approval behavior, and the impact of each failure.
- Decision rule: acceptance criteria for the intended use, including how severe failures are treated and which limitations remain.
State clearly that a passing result means the tested configuration resisted the tested attacks under the stated conditions. It does not establish resistance to untested attacks, longer interactions, different data, new tool configurations, or changed safeguards. If the system changes, the old report describes the old configuration.
How to use current guidance carefully
NIST CAISSI’s Guidelines page, updated September 30, 2026, lists Practices for Automated Benchmark Evaluations of Language Models as an initial public draft with preliminary practices for evaluating language models and agent systems. Treat draft guidance as draft, and verify its status when relying on it. It can inform evaluation planning, but it does not turn a benchmark score into a general safety certification.
Across guidance and results, the practical standard is the same: define the real system boundary, test realistic attacks in isolation, measure outcomes at the level of individual tasks, and constrain access according to the risk. A benchmark result is meaningful only alongside the setup that produced it.
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