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What “safe tool access” means
An AI agent with tool access can do more than produce text. Depending on its tools and permissions, it may read files or messages, browse webpages, send communications, run code, or change records. That makes its safety a property of the whole system: the model, its instructions, available tools, access rights, approval steps, and runtime safeguards all matter.
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A central risk is agent hijacking. An attacker puts instructions in material the agent may read—such as an email, webpage, file, or tool response—intending to override the user’s request. For example, a user might ask an agent to summarize a message while the message itself tells the agent to forward confidential information. A useful evaluation checks whether the agent completes the legitimate task without following the injected instruction.
Safety is not simply whether the agent says it will ignore an attack. The evaluation should establish whether it actually attempts or completes the injected action, whether data crosses its authorized boundary, whether it seeks approval before consequential actions, and what harm could result if the attempt succeeds. Consider confidentiality, integrity (unauthorized changes), and availability (deletion or disruption).
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That distinction matters when interpreting a trace. A refusal to follow an instruction is different from an attempted action blocked by a permission boundary, and both differ from an action that succeeded. Record the point at which the system stopped: for example, whether it never called a tool, called a tool that was denied, paused for confirmation, or completed the operation. Those details show whether the model, tool permissions, or approval control prevented the harm.
Test the deployed system, not only the model
Evaluate the version users will actually encounter, including its model, system and developer instructions, tool definitions, connected services, permissions, approval gates, and other runtime safeguards. A model-only result can help characterize model behavior, but it does not predict every outcome of a configured agent. In its ChatGPT Agent System Card, OpenAI distinguishes model-behavior testing from the complete end-to-end prompt-injection mitigation stack.
Keep those result types separate when comparing configurations. If one result is for a model in a controlled challenge and another is for an agent with tools and safeguards, they do not measure the same thing. Record the tested configuration alongside every result so readers can tell what the evidence covers.
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Build a controlled, realistic evaluation
1. Map access and possible harms
Start with an inventory of what the system can access and change. Include each tool, the data it can read, the systems it can write to, and the identity and permission level it uses. Identify actions that could cause meaningful harm: sending a message, disclosing sensitive data, executing code, changing a record, or deleting a document, for example. Risk depends partly on excessive agency: too many functions, overly broad permissions, or too much autonomy.
Define what counts as an unauthorized action before testing. An agent might encounter an injected instruction but decline it; it might begin preparing an action but stop at an approval gate; or it might complete the action. Those are different outcomes. Decide in advance how to classify them and what harm each could cause.
Make the boundary concrete for each workflow. Specify which files, accounts, records, or messages are in scope; which operations are read-only; and which changes require approval. If the user asks for a summary, for instance, reading the relevant content may be authorized while forwarding it, changing permissions, or deleting it is not. Clear criteria make it possible to distinguish useful task completion from an unauthorized side effect.
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For each normal workflow, create a benign user task and place an adversarial instruction in external content the agent is expected to inspect. Make the injected instruction relevant to the tools and data available in that workflow. Test attempts to redirect the task, disclose data, trigger code execution, send messages, or change or delete records where those actions are in scope.
Do not evaluate only obvious or generic attacks. Include realistic variations in the material the agent reads and attacks tailored to the system’s own tools and workflow. The test should reveal whether the agent treats untrusted content as data or follows it as an instruction. NIST’s Center for AI Standards and Innovation (CAISI) recommends adaptive evaluations and task-specific analysis; its technical staff wrote, “Evaluations need to be adaptive. Even as new systems address previously known attacks, red teaming can reveal other weaknesses.”
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A useful scenario pairs one clear user goal with one malicious instruction that conflicts with it. For example, ask the agent to find travel details in a test inbox, while a message includes a request to send account information elsewhere. Record whether it retrieves the requested details, whether it attempts the extra action, and whether the action is blocked or approved. Vary the attack’s wording and location—such as an email body, webpage, file, or tool response—without changing the authorized task. This helps distinguish a broad failure pattern from a single phrasing-specific result.
Include attacks that reflect the system’s actual capabilities rather than testing every imaginable action indiscriminately. A read-only agent cannot delete records through its configured tools; an agent connected to a shell, database, or messaging tool may face different consequences. NIST CAISI’s examples include remote code execution, database exfiltration, and automated phishing, illustrating why attack scenarios should be matched to available tools and potential impact.
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3. Isolate tests and use controlled data
Run the evaluation in a sandbox, away from real user accounts and production data. Use simulated environments where possible, and seed them with test data that lets you detect attempted or successful access beyond the task’s authorization. The goal is to exercise realistic tool behavior without exposing real information or causing real-world side effects.
NIST CAISI’s experiments used AgentDojo, which includes simulated Workspace, Travel, Slack, and Banking environments. CAISI also added scenarios for remote code execution, database exfiltration, and automated phishing. These are examples of evaluation settings and attack types, not proof that every tool-enabled system has the same risks or defenses.
Before a run, verify that the sandbox is actually isolated: test credentials should not grant access to production, test messages should not reach real recipients, and simulated records should not be linked to live systems. Use recognizable test data or markers so a reviewer can tell whether an agent accessed or transmitted something outside the task’s permitted scope. If a tool can cause an external side effect, configure a safe substitute or a block that records the attempted action without carrying it out.
4. Repeat attempts and adapt attacks
Run each scenario more than once. Agent behavior can vary from one attempt to another, so a single successful defense—or a single failure—does not describe the full pattern. Start with known attack patterns, then have red teamers develop new attempts against the particular system. Keep the scenario set current as models, tools, and attack techniques change.
Track the number of attempts for each scenario and preserve the conditions for each run. If an agent succeeds in resisting an attack on most attempts but fails once in a way that could expose sensitive data, the occasional severe failure should not disappear inside an overall average.
Keep the benign task constant when comparing attack variations, and record the exact injected content and tool responses. A reviewer should be able to tell whether one configuration saw a harder attack, had access to a different connector, or was tested under a different approval setting. Repeated runs are not a substitute for adaptive red teaming: repetition measures variation on scenarios already chosen, while adaptive testing seeks weaknesses that the initial set missed.
5. Retest after material changes
Repeat relevant tests when the model, prompts, tool definitions, permissions, connectors, or safeguards change. Any of these can alter what the agent can do or how it responds to untrusted content. Keep earlier results for comparison, but label them with their original configuration and evaluation date rather than treating them as evidence about a changed system.
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Retesting should follow the change in risk. A new read-only connector may call for scenarios about additional data exposure; a new write or send capability calls for testing authorization, approval, and side effects. Even if the model has not changed, adding a broader tool or changing the identity it uses can change the impact of the same hijacking attempt.
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What to measure and report
Report ordinary task performance, resistance to hijacking, and impact as distinct dimensions. A system that refuses every task may avoid injected actions but still fail its intended purpose; a system that completes tasks quickly may still be unsafe if it obeys malicious content. An aggregate score can conceal both problems.
| Measure | What to record | Why it matters |
|---|---|---|
| Legitimate task completion | Whether the requested task was completed, and whether the result met the task’s requirements. | Shows whether safety behavior preserves useful performance rather than simply blocking work. |
| Injected-task outcome | Whether the agent ignored, attempted, or completed the malicious instruction. | Separates exposure to an attack from actions that could produce harm. |
| Data boundary | Whether data was accessed or sent outside the authorization defined for the test. | Captures confidentiality failures that may not be visible in a simple task-success score. |
| Approval behavior | Whether the agent requested the required user approval before a consequential operation. | Shows whether a human gate works at the point where it can prevent an unauthorized side effect. |
| Potential impact | The plausible severity if the action succeeded, such as disclosure, unauthorized change, deletion, or disruption. | Prevents a low-impact event and a severe event from being treated as equivalent failures. |
| Repeatability and adaptation | Attempts per scenario, outcomes across attempts, and results against newly developed attacks. | Shows whether a result is consistent and whether defenses hold against attacks tailored to the system. |
Also document the model and system configuration, available tools and their permissions, active safeguards, test environment, scenarios, number of attempts, and evaluation date. Give task-level results as well as any aggregate. When comparing two configurations, use the same task distribution and report the same axes: attack success by task and scenario, impact severity, data sensitivity and tool scope, approval behavior, repeated and adaptive attack performance, and normal-task completion. Keep model-only and full-system results distinct.
For a useful report, show counts as well as percentages where possible: the number of attempts, the number in which the injected action was attempted, and the number in which it succeeded. Break these out by scenario and impact rather than presenting only a pooled average. State how an attempted action was counted, including whether a tool call was blocked or an approval request was generated. This makes the result interpretable without implying that an aggregate captures every operational risk.
Keep enough detail to reproduce and review the evaluation: the tested model and tool configuration, permission scope, active guardrails and approval gates, environment, tasks and attack variants, attempts per scenario, and date. When reporting an incident or near miss, describe the action and its plausible impact, not only a score. Do not merge a harmless blocked attempt and a successful sensitive-data transfer into one undifferentiated failure count.
How to interpret published results
Published figures can illustrate why the method matters, but they are not universal safety rates. NIST CAISI reported an 11% baseline attack-success result and 81% for its strongest novel attack in a particular 2025 Workspace red-team experiment. It tested an upgraded Claude 3.5 Sonnet agent using attacks developed for that system and held-out user tasks. Those figures describe that experiment, not the expected attack-success rate of agents generally.
In the same work, NIST reported 57% average success across five specific injection tasks. Because those tasks differed in potential impact, the average alone did not show the risk of each outcome. This is why evaluations should expose per-task results and impact, not just a single percentage.
OpenAI’s ChatGPT Agent System Card reports 99.5% success at disregarding irrelevant instructions in a synthetic text-browser challenge set. The page reviewed does not state a publication date for the system card. OpenAI also notes that this model-behavior result does not represent its complete end-to-end prompt-injection mitigation stack. It should not be compared directly with a tool-connected red-team experiment as if both measured the same setup.
No reviewed source establishes a universal benchmark score or certification threshold for “safe tool access.” Set acceptance criteria for the specific use case, access level, and potential harms, then disclose those criteria. A threshold for an agent that can only read public information would not automatically be suitable for one able to send messages, access sensitive records, or delete files.
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When setting a threshold, state what failure rate or severity is unacceptable for the intended deployment, and what happens if the system does not meet that bar. A deployment decision can also require additional controls rather than a binary pass: narrow a permission, remove a tool, add a confirmation step, or keep the agent in a supervised setting. The relevant question is whether the tested configuration is proportionate to the task and its possible harms, not whether a headline percentage sounds high.
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Limit what tools can do
OWASP’s Gen AI Security Project recommends minimizing the extensions available to an agent, the functions those extensions expose, and the permissions they receive. Give the agent only the capabilities required for the task. When a narrow function can do the job, prefer it over a general-purpose capability: for instance, a tool that writes one specified file is easier to bound than an open-ended shell tool.
For downstream access, use the user’s identity and the minimum privileges needed for the task. A broad service account can turn a prompt-injection failure into a much larger incident than a narrowly scoped identity would allow. Permission design should limit the potential damage even if the agent makes a mistake.
Apply least privilege at more than one level: remove unnecessary tools, restrict what each remaining function can do, and limit the data and systems the agent can reach. For example, a workflow that needs to draft a response may not need permission to send it; the user can review and send it separately. If sending is required, a narrowly scoped send function and an explicit confirmation can reduce the consequences of an erroneous action.
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Require independent approval for high-impact operations, such as deleting documents. The approval should be an actual decision point before the action, not merely a notice after it. Test whether the agent pauses for approval in the scenarios where it should, including when an injected instruction asks it to take the action.
Approval behavior should be measured as part of the system, not assumed from a prompt that tells the model to ask. Verify that the gate is active, that the user can understand what operation is being authorized, and that the operation does not happen until approval is given. Where an action is too risky to delegate, removing the capability may be more reliable than relying on a confirmation prompt.
Constrain information passed between steps
OpenAI’s agent-building guidance recommends keeping tool approvals enabled, using structured outputs to constrain information passed between steps, applying guardrails, and grading traces and evaluations. These practices can reduce risk, but the guidance also warns that they do not eliminate mistakes or susceptibility to prompt injection.
Structured outputs can make handoffs more predictable by limiting the form of information passed from one step to another. They are not proof that the information is trustworthy or that a later step will use it safely. Review traces and evaluation results for unexpected tool calls and failures, and keep guardrails and approval mechanisms in place for actions with meaningful consequences.
The particular OpenAI documentation page reviewed says Agent Builder is being deprecated and is scheduled to shut down November 30, 2026. That date is relevant to that interface, not a reason to treat the broader safety practices as interface-specific instructions. Check current documentation before relying on a product workflow or label.
Turn results into a deployment decision
Use evaluation results to decide whether a particular system is appropriate for a particular access level—not whether AI agents are safe in the abstract. A defensible decision connects the test evidence to the assets at risk, the permissions granted, and the consequences of failure.
- Restrict access or autonomy if an agent can attempt harmful operations without a reliable approval gate.
- Improve the control or scenario if testing reveals a repeatable route from untrusted content to an unauthorized action.
- Set explicit acceptance criteria based on the use case and the severity of possible outcomes; explain how task completion and harmful actions are weighed.
- Document residual risk where testing cannot cover every attack, interaction length, dataset, or future configuration.
- Schedule reassessment when access, tools, safeguards, or system components change.
NIST CAISSI’s Guidelines page, updated September 30, 2026, lists Practices for Automated Benchmark Evaluations of Language Models as an initial public draft containing preliminary practices for evaluating language models and agent systems. Treat draft guidance as provisional and check its status when applying it.
Make the decision specific to the deployment: name the workflows approved for use, the tools and permissions they require, the controls that must remain enabled, and the conditions that trigger reassessment. A result from a sandbox is evidence about that tested setup; it does not cover a later configuration with broader access or a different connector. If the system is changed, either rerun relevant scenarios or clearly limit the claim to the earlier version.
What a passing test can—and cannot—establish
A passing result means the tested system resisted the attacks tried under the conditions recorded. It does not establish resistance to attacks that were not tested, longer interactions, different data, new tools, changed permissions, or revised safeguards. The practical value of an evaluation comes from making that boundary visible: report the setup, task-level outcomes, attempts, and impact so that the result can inform a proportionate decision rather than imply a universal guarantee.
There is no shortcut from one strong benchmark figure to a claim of safe tool use in every setting. The strongest evaluation combines realistic benign work, adversarial content placed in the agent’s information stream, repeated and adaptive attempts, impact-aware reporting, and controls that limit what a failure can do. Reassess when the system or its access changes, and treat each result as scoped evidence for the configuration that was actually tested.
Frequently Asked Questions
How are you testing an AI agent for security before users rely on it?
Pair ordinary user tasks with adversarial instructions embedded in realistic emails, webpages, files, or tool responses. Run the configured agent in a sandbox with controlled data, repeat each scenario, and record both legitimate task completion and whether the injected action was attempted or completed. Include the outcome, impact, approval behavior, tool permissions, safeguards, and test date in the report.
Should I test the model alone or the full agent?
Test the full system users will encounter, including the model, instructions, tools, connected services, permissions, approval gates, and runtime safeguards. A model-only result can describe model behavior, but it does not establish how the configured agent behaves end to end. Keep the two result types separate when reporting or comparing systems.
What counts as a prompt-injection or agent-hijacking failure?
Classify outcomes separately: the agent may ignore the instruction, attempt an action that a permission or approval gate blocks, or complete the action. Also record whether data crossed its authorized boundary and the plausible impact. A blocked attempt is different from a successful disclosure, change, deletion, or other side effect.
How many times should each scenario be tested?
Run each scenario more than once because behavior can vary between attempts, and report the number of attempts and outcomes. There is no universal attempt count or pass threshold established here. Repeat testing should be paired with adaptive red teaming that creates new attacks against the particular system.
What is a safe pass score for tool access?
There is no universal benchmark pass score or certification threshold for safe tool access. Set and disclose acceptance criteria for the specific task, data sensitivity, permissions, and potential harms. Report task-level results and severity as well as any aggregate percentage.
Can sandbox results guarantee that an agent is safe in production?
No. A passing result applies to the tested configuration, environment, scenarios, and attempts. It does not establish resistance to untested attacks, longer interactions, different data, new tools, or changed safeguards. Use isolated tests to reduce risk, then preserve least privilege, approval gates, and reassessment as the system changes.
The Bottom Line
Evaluate the configured agent in an isolated environment with realistic tasks and adaptive prompt-injection attempts. Report normal-task completion, attack outcomes, data boundaries, approval behavior, and impact separately across repeated runs. Pair the evidence with least-privilege tools and independent approval for consequential actions; no benchmark result is a universal guarantee.
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