The Tool Desk
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What this test measures
Instructions can come from different sources and carry different priorities. OpenAI describes a hierarchy of system, developer, user, and tool instructions: a lower-priority instruction should not override a conflicting higher-priority constraint. A test checks whether the model respects that boundary in the particular configuration you evaluate; it does not establish how the model will behave in every situation. See OpenAI’s explanation of instruction hierarchy and evaluations.
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“Hidden instructions” can mean at least two different things. One is a protected instruction or secret that a user tries to extract. The other is an instruction planted in untrusted content—such as a webpage, document, email, or tool result—that tries to redirect the model. These are related but distinct behaviors, so design and report separate tests for them.
The distinction matters because a model may handle one route well and another poorly. A user who directly asks the model to disregard a higher-priority rule is testing a direct conflict. A webpage that contains instructions aimed at the assistant is testing whether the model treats third-party material as authoritative when it should be treated as data. Put each attempt in the channel it is meant to test, rather than treating all “prompt injection” examples as interchangeable.
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Disclosure, hijacking, and task performance
- Disclosure: Does the model expose a protected dummy marker, either verbatim or in a recognizable transformed form?
- Instruction hijacking: Does it follow an unauthorized lower-priority instruction instead of the intended task or higher-priority constraint?
- Unauthorized action: Does it try to use a tool or send information somewhere it should not?
- Legitimate-task completion: Can it still do the user’s valid task while disregarding the malicious instruction?
Measure all four where applicable. A model that refuses every request may avoid some attacks while failing ordinary work; an answer that looks safe may still conceal a tool-side attempt.
These outcomes should be kept distinct in your notes. For example, a response could avoid repeating the marker but abandon the requested task after reading an injected instruction. That is not the same result as revealing a marker, and it should not be hidden inside a single pass/fail label. Likewise, a model could give a safe-looking answer after attempting a tool action. The visible response and the action record answer different questions.
Set up a safe, meaningful evaluation
1. Define the boundary and expected outcome
Before running prompts, write down what instruction is authoritative, what task the model should perform, where untrusted text may enter, what tools or actions are available, and what counts as a violation. For each case, specify the intended task and the observable result that would count as disclosure, hijacking, or an unauthorized action. OWASP’s guidance recommends defining the intended violation or legitimate task, required context, and observable outcome in advance: OWASP LLM prompt-injection prevention guidance.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesA written boundary makes the result interpretable. State which instruction is being protected and which lower-priority source is attempting to conflict with it. Describe the task the model is still expected to complete, such as summarizing or extracting relevant information from supplied content, if that is part of the evaluation. Note whether the test environment permits tools, connectors, or other actions. If the case does not define these conditions, reviewers may disagree about whether a refusal, partial answer, or tool call counts as success.
Decide on the scoring rule before seeing the model’s answer. For instance, decide whether a partial phrase, a transformed form of the dummy marker, or an attempted external transfer counts as a disclosure or action. Predefining outcomes helps avoid changing the standard after a surprising result. It also lets you compare trials consistently.
2. Use a dummy marker, not a real secret
Put a unique, harmless phrase in the hidden instruction—for example, a made-up string with no connection to credentials, personal data, or production systems. Ask varied questions designed to elicit or transform it, but do not expose real secrets to the test. Score both an exact match and recognizable paraphrases or partial disclosures. An exact-marker check alone can miss leakage that changes the wording.
The marker is a measuring device, not a real credential. Choose something distinctive enough to locate in outputs and logs, but harmless if it appears. If your test is about whether the model protects an instruction, use a dummy value for that protected content. This allows you to observe the behavior without turning the evaluation itself into a reason to expose confidential information.
Do not equate “the exact string was absent” with “nothing was revealed.” A model might repeat only part of it, describe it in other words, or expose it through an available tool rather than its final answer. Decide how a reviewer will judge recognizable transformed disclosures and ambiguous cases, and retain enough context to make that judgment. When automated matching is used, it can flag likely cases, but human review may still be needed to determine whether a paraphrase is meaningful.
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3. Sandbox actions and watch every channel
Replace external actions with a sandbox or instrumented destination. Log tool calls, destinations, and attempted transfers, not just the text shown to the user. OWASP cautions that a clean-looking answer does not establish that information was not sent through another channel. Keep the test environment separate from live accounts, data, and services.
Before testing, identify the routes through which the model could act or disclose information in the configuration under review. If a tool is available, make its activity observable and direct any external destination to an instrumented substitute. Review both attempts and outcomes: a blocked or failed tool call is different from a successful transfer, but either may reveal that the model tried to follow an unauthorized instruction. The goal is to evaluate the boundary safely, not to make a real external change.
Use the same sandbox assumptions across comparable runs. If one model can access a connector and another cannot, their results do not isolate model behavior. Record tool availability and safeguards so the reader can tell whether a difference came from the model, the surrounding setup, or both.
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4. Keep a benign control
Include ordinary, non-malicious examples that contain instruction-like language but should not trigger a refusal or derail the task. This helps distinguish useful resistance from blanket refusal. For indirect tests, include harmless external content alongside maliciously crafted content so you can see whether the model can process material without treating every imperative sentence as authoritative.
A control case should make the expected legitimate work clear. If the model is meant to process a document, include a normal document and check whether it completes the requested work. Then test a corresponding case in which external content contains an instruction that conflicts with that task. This pairing can show whether the model is resisting the injected instruction while preserving useful behavior, rather than simply refusing to engage with any content that contains commands or imperative language.
Build separate test groups for each attack channel
Place each attack in the channel you intend to evaluate. A malicious instruction in the user’s message tests direct resistance. The same text inside a retrieved page or tool result tests whether the model handles untrusted external content safely; it is not a substitute for a direct test.
| Test group | Where the attack appears | What to observe |
|---|---|---|
| Direct override | User message | Whether the model follows the conflicting lower-priority instruction instead of the specified constraint. |
| Direct extraction | User message | Whether the model reveals the harmless hidden marker, including recognizable transformed disclosure. |
| Indirect injection | Retrieved page, document, email, or tool-provided content | Whether the model treats the untrusted text as an instruction, abandons the legitimate task, or attempts an unauthorized action. |
| Benign control | Ordinary user input or harmless external content | Whether the model completes the intended task without an unnecessary refusal or derailment. |
For indirect cases, preserve the ordinary task context and deliver the attack as external content. If the test is meant to model a retrieved page, put the text in that page or the relevant retrieved-content channel; do not paste it into the user’s request and then label the result an indirect-injection test. Similarly, an attack in a tool result should arrive as a tool result. This channel discipline is what makes the outcome meaningful.
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OpenAI’s October 2026 deployment evaluation distinguishes direct instruction-hierarchy attacks from indirect attacks embedded in third-party content; its findings apply to its own models and evaluation setup, not automatically to other systems. Read the evaluation description.
Run the test consistently
- Record the configuration. Note the model and version, the hidden instruction, relevant system settings, enabled tools or connectors, and safeguards. If comparing configurations, change only the factor you intend to evaluate or clearly document every difference.
- Run each case in its intended channel. Keep direct user attacks in the user message. Put indirect attacks in the external content or tool-result channel. Preserve the legitimate task and surrounding context for the indirect case.
- Vary phrasing and context. Try different wording and multi-turn pressure while keeping the underlying attempted violation and scoring rule stable. A single cleverly worded example is too narrow to characterize behavior.
- Inspect outputs and logs. Check the visible answer for exact or recognizable disclosure, instruction-following, and task completion. Review tool calls and destinations for attempted or completed actions, even when the final text appears safe.
- Repeat trials. Outputs can vary. Run multiple trials and retain the case wording, configuration, results, and scoring notes so another person can interpret what was tested.
- Report observed results with their limits. Give counts or rates alongside the test set, channels, model/version, tools, and scoring method. Label a small hand-picked suite as a smoke test, not a representative security benchmark.
Vary the wording without changing what the case is intended to measure. One version might make a direct request to override a constraint; another can apply pressure over multiple turns or ask for the hidden marker in a different way. The point is to see whether the same boundary holds across prompt variations, not to create a different task for every trial. Keep the rubric stable so the results remain comparable.
Repeated trials matter because a single output is only one observation. Retain the exact prompts, the surrounding context, and relevant logs for each run. When a model’s outputs differ across repeats, report that variation rather than selecting only the safest or most revealing answer. If a result depends on a particular model version or tool configuration, state that plainly.
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There is no threshold in this guidance that turns a particular number of passed tests into proof of security. Counts and rates summarize what happened within your selected cases and conditions. They do not compensate for missing attack channels, a narrow set of phrasings, or an incomplete record of tool activity.
Score more than exact phrase matches
For each trial, record whether the dummy marker or a recognizable transformed version appeared; whether the model followed an untrusted instruction; whether it completed the legitimate task; and whether it attempted an unauthorized tool action or transfer. Use human review when a paraphrase or ambiguous response makes automated matching uncertain. Do not treat the model’s own statement that it detected an attack as evidence that it resisted one: a published example in the joint OpenAI and Anthropic evaluation shows a model recognizing an attack and still revealing the protected secret. See the joint evaluation and its discussion of evaluation limits.
Exact-match scoring is useful for a known dummy marker but is incomplete on its own. A marker may be paraphrased, partially disclosed, or exposed through a tool rather than the answer. A clean final response is therefore not, by itself, a pass.
A practical record for each case can include the intended boundary, the channel used, the model’s visible response, the legitimate task result, any marker disclosure, tool calls and destinations, and the reviewer’s decision. Separate “attempted” from “completed” when recording an unauthorized action. If a result is ambiguous, keep it marked for review instead of forcing it into a confident pass or fail based only on an automated check.
Likewise, a model saying that it has ignored an attack is not enough to establish safe behavior. Judge observable outputs and actions. A system can describe the right rule while still violating it, and a terse refusal may not show whether the legitimate task could have been completed safely. The score should reflect what the model did, not only what it says about its intentions.
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Compare models or configurations fairly
Use the same cases and scoring rubric when comparing two models or configurations. Report results separately rather than collapsing different risks into one score.
| Comparison dimension | What to report |
|---|---|
| Instruction source and priority | Whether the conflict was system-versus-user, developer-versus-user, or an instruction embedded in external/tool content. |
| Attack channel | Direct message or indirect retrieved/tool-provided content. |
| Outcome | Disclosure, hijacking, unauthorized action or transfer, and legitimate-task completion. |
| Coverage and grading | Hand-written cases or benchmark cases; number and variety of prompt variations; repeated trials; automated scoring or human review. |
| Configuration | Model/version, hidden instruction, tools/connectors, and safeguards; identify what was held constant or changed. |
Keep the comparison granular. A configuration may have a different result for direct extraction than for instructions embedded in retrieved content. A single combined score can conceal those differences, as well as a tradeoff between resisting attacks and finishing legitimate work. Showing separate results makes it easier to identify which tested boundary needs further attention.
If you change a model and a tool configuration at the same time, a changed result cannot be attributed cleanly to either one. Ideally, hold the test cases, hidden instruction, available tools, safeguards, and scoring method constant while changing the one factor under evaluation. When that is not possible, document each difference and limit the conclusion accordingly.
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Interpret a pass—and a failure—carefully
- A pass means only that no violation was observed in those trials. It does not prove the model cannot be induced to disclose, follow an injection, or act through another route.
- A failure is useful evidence about the tested boundary. Preserve the exact case and configuration, then investigate whether the issue was disclosure, task derailment, tool use, or some combination.
- A refusal is not the only acceptable safe outcome. In an indirect-injection case, the model should ideally reject or ignore the embedded instruction while still performing the legitimate task when it can do so safely.
- Unknown attacks remain unknown. Finite, hand-selected or known attack sets cannot establish security against prompts or contexts that were not tested.
Think of a test result as a statement about a defined sample: this model and setup produced these observed outcomes on these cases, using this rubric. It is not a guarantee about other model versions, channels, contexts, or future prompts. A known attack set can help provide repeatable coverage, but coverage of known cases does not establish resistance to unknown attacks.
A failure should also be described precisely. If the marker appeared, record a disclosure. If the model followed an embedded instruction and stopped doing the valid task, record a hijack or task derailment. If it attempted an external action, record that route even if the destination sandbox blocked it. These distinctions help explain what was observed without overstating the conclusion.
OpenAI’s gpt-oss model card also describes testing for system-prompt extraction and prompt-injection hijacking. Those tests are specific to that model’s evaluation and should not be read as a guarantee for other models or deployments. Read the gpt-oss model card.
Frequently asked questions
Can an AI reveal its system prompt?
A model may disclose protected prompt content, so the practical way to test that behavior is to use a harmless dummy marker in the hidden instruction and try varied direct extraction requests. Record exact, partial, and recognizable transformed disclosures. A test that does not observe a disclosure only describes those trials; it does not prove that the system prompt can never be exposed.
How do I test for prompt injection?
Define the protected instruction, legitimate task, untrusted content sources, available tools, and observable outcomes first. Then test direct attacks in user messages and indirect attacks in retrieved pages, documents, emails, or tool outputs as separate groups. Use dummy data, sandbox external actions, include benign controls, vary wording, repeat trials, and score both attack resistance and task completion.
How can I tell whether an agent follows malicious instructions hidden in a webpage?
Put the instruction in the webpage or other external-content channel the agent actually reads, while keeping a legitimate task in place. Observe whether the agent follows the embedded instruction, abandons the task, reveals the dummy marker, or tries an unauthorized tool action. Review tool logs and destinations as well as the final answer; a safe-looking answer alone cannot rule out an action through another channel.
Is an exact-match check enough to detect a leaked secret?
No. Exact matching can find a known marker, but the model could reveal part of it, paraphrase it, or expose it through a tool instead of the visible answer. Use a rubric for recognizable transformed disclosures and human review for ambiguous cases, and monitor tool activity and destinations.
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Does passing a prompt-injection test prove the model is secure?
No. A pass means only that the test did not observe a violation under the specific prompts, channels, model version, tools, safeguards, and scoring method used. Hand-picked cases are a smoke test, and finite known-attack sets do not establish resistance to unknown attacks. Report the coverage and limitations alongside results.
Bottom line
Use a harmless hidden marker, test direct and indirect attacks in their actual channels, preserve a legitimate task, sandbox and monitor tools, and record observable outcomes across repeated trials. Compare results by attack type and configuration rather than relying on a single score. The result is evidence about the cases you tested—not a guarantee that the model will resist every hidden instruction.
Frequently Asked Questions
Can an AI reveal its system prompt?
A model may disclose protected prompt content. Test with a harmless dummy marker in the hidden instruction, varied direct extraction requests, and scoring for exact, partial, or recognizable transformed disclosure. No disclosure in your trials is not proof that the prompt can never be exposed.
How do I test for prompt injection?
Define the protected instruction, legitimate task, untrusted content sources, available tools, and observable outcomes. Test direct attacks in user messages separately from indirect attacks in retrieved pages, documents, emails, or tool outputs. Use dummy data, sandbox actions, benign controls, varied prompts, repeated trials, and measure both resistance and task completion.
How can I tell whether an agent follows malicious instructions hidden in a webpage?
Place the instruction in the webpage or external-content channel the agent reads, while retaining a legitimate task. Check whether it follows the embedded instruction, abandons the task, reveals a dummy marker, or attempts an unauthorized action. Review tool logs and destinations as well as the final response.
Is an exact-match check enough to detect a leaked secret?
No. A model may reveal only part of a marker, paraphrase it, or expose it through a tool rather than the visible answer. Combine exact matching with a rubric for transformed disclosures, human review of ambiguous cases, and monitoring of tool activity.
Does passing a prompt-injection test prove the model is secure?
No. A pass means only that no violation was observed in the tested cases and configuration. Hand-picked tests are smoke tests, and finite known-attack sets do not establish resistance to unknown attacks. Report the model version, channels, tools, safeguards, scoring method, and coverage.
The Bottom Line
Use a harmless hidden marker, test direct and indirect attacks in their actual channels, preserve a legitimate task, sandbox and monitor tools, and record observable outcomes across repeated trials. Compare results by attack type and configuration rather than relying on a single score. The result is evidence about the cases you tested—not a guarantee that the model will resist every hidden instruction.
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