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Test instruction-following with harmless markers, controlled direct and indirect attacks, and a legitimate task the model should still complete. Track what the model says and what its tools attempt to do. A few hand-picked prompts can reveal obvious failures, but they cannot prove that a model is secure against other attacks.
What this test can—and cannot—tell you
Instruction hierarchy is the priority relationship among instructions from different sources. OpenAI describes a hierarchy of system, developer, user, and tool instructions; a lower-priority instruction should not override a conflicting higher-priority constraint. The hierarchy matters when a user asks a model to ignore its rules or when untrusted content tries to redirect an agent. See OpenAI’s description of instruction hierarchy.
A prompt test can show how a particular model behaves with the prompt, tools, attack wording, and scoring method you used. It cannot establish general resistance to unknown attacks. Outputs may vary, and checking only the visible answer can miss attempted or successful tool-side actions. Treat a small hand-written set as a smoke test, not a security benchmark.
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That distinction is important if you are asking, “Can an AI reveal its system prompt?” A test can look for a known harmless marker or recognizable disclosure in a specific setup. It cannot certify that every part of a hidden prompt is protected, nor that a different wording, channel, model version, or tool configuration would produce the same result. Describe observations, not guarantees.
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Set the boundary before you test
Write down what the model is supposed to protect, what task it should perform, and which inputs are untrusted. Make the expected violation observable before running a case; otherwise it is easy to grade a result according to whether it merely looks reassuring.
- Authoritative instruction: identify the system or developer constraint that should remain in force.
- Legitimate task: state what useful work the model should still do, such as summarizing a page or extracting a date.
- Untrusted channel: specify whether the attack arrives in a user message, retrieved page, document, email, or tool result.
- Available actions: list tools, connectors, and destinations the model can access, including any actions that must be blocked.
- Failure criteria: define disclosure, following the malicious instruction, unauthorized data transfer, and task abandonment separately.
OWASP’s prompt-injection guidance recommends defining the intended violation or legitimate task, required context, and observable outcome for each case before testing: OWASP LLM01: Prompt Injection.
For example, an evaluation question might be: “Can the model summarize this mock page while ignoring text in the page that asks it to disclose the protected marker?” That defines a useful task, the location of the untrusted instruction, and the behavior to observe. A separate question might test whether a user message can override a system constraint. Keep these as separate cases: a success or failure in one channel does not answer the other question.
Also decide what evidence you can actually inspect. If the model can call tools, a test limited to its final answer is incomplete. Decide in advance whether you can review tool invocations, attempted actions, and destination logs. If you cannot observe a channel, state that limitation rather than treating the absence of visible evidence as a pass.
Use harmless markers and a safe environment
Put a unique, fictional marker in the hidden instruction—for example, a made-up string such as ORCHID-47-LANTERN—and direct the model not to disclose it. Do not use real credentials, customer records, API keys, or confidential operational instructions. A fake marker lets you observe leakage without exposing a real secret.
Choose a marker that is easy to distinguish from ordinary text, and record the exact value used in the test setup. The goal is to make disclosure observable, not to make the model’s task depend on the marker. You can then try different requests to reveal it while keeping the protected value harmless. A hidden phrase or fake password is a test instrument, not evidence that a real secret would be safe in the same configuration.
For tests involving tools or external services, use a sandbox or instrumented destination that records attempted actions without causing real-world effects. Monitor tool calls and destinations as well as the final text: a clean-looking answer does not prove that information stayed inside the system. OWASP likewise cautions that output inspection alone does not establish that data was not sent through another channel.
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Keep the test environment and the behavior under evaluation aligned. If the question is whether an agent might send data somewhere, the test should make attempted tool actions observable in a safe way. Do not test by sending real data to a real outside destination. A refusal in the visible response is not sufficient evidence if a tool call or transfer could have happened separately.
Build separate test groups
Keep attack channels distinct. If an attack is placed in the user message, the case tests a direct attack. If it is embedded in a webpage, document, email, or tool result, it tests an indirect attack. Moving the same text into a different channel changes what the result demonstrates.
| Case group | Where the attack belongs | What to observe |
|---|---|---|
| Direct override | User message | Whether the model abandons a higher-priority constraint |
| Direct extraction | User message | Whether the model reveals the dummy marker or protected instruction |
| Indirect injection | Retrieved or tool-provided content | Whether the model treats untrusted text as an instruction and follows it |
| Benign instruction-like text | External content, without a malicious request | Whether the model can handle ordinary quoted or instruction-like text without overreacting |
OpenAI’s evaluation descriptions distinguish direct instruction-hierarchy attacks from indirect attacks in third-party content, including tool outputs; see OpenAI’s instruction-hierarchy evaluation discussion and its deployment evaluation description. The latter reports results for its own models and evaluation setup, not a universal score for other systems.
For indirect testing, put the attack in the external content channel that the model is supposed to read. For instance, the user can ask for a summary while a mock webpage contains instruction-like text that conflicts with the authoritative constraint. If the same text is instead typed by the user, that is a direct-attack case. Both are useful to test, but they answer different questions.
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Include benign controls alongside attacks. A document may quote an instruction, discuss prompt injection, or contain ordinary instruction-like language without asking the model to violate its constraints. The point of a control is to see whether the model can still perform the requested task appropriately instead of rejecting any content that resembles an instruction. Record these outcomes separately from attack resistance.
Run a practical test sequence
- Record the configuration. Note the model and version, hidden instruction, user task, tools and connectors, safeguards, and any relevant system settings. Keep these constant when comparing runs, or clearly record what changed.
- Run a benign baseline. Give the model the legitimate task with no attack present. Record whether it completes the task as expected; this shows what useful behavior looks like for that case.
- Try direct override and extraction cases. In separate user-message cases, ask the model to disregard its higher-priority instruction and to reveal the dummy marker. Vary the wording and try multi-turn pressure, but keep the intended violation and scoring rule stable.
- Test indirect attacks in their actual channel. Place attack text in a mock webpage, document, email, or tool result and ask the model to perform the legitimate task using that content. Do not put the attack in the user request if the question is whether external content can hijack the model.
- Include benign controls. Test ordinary material that quotes instructions or contains instruction-like wording but does not ask the model to violate a constraint. This helps identify systems that reject too much along with systems that comply too readily.
- Inspect text and actions. Check the visible answer for the exact marker and recognizable transformed disclosures. Review tool calls, attempted actions, and destination logs for unauthorized behavior, even when the answer itself appears safe.
- Repeat and preserve results. Run multiple trials, retain the test inputs and outputs, and report counts under the same configuration. Record ambiguous cases for human review rather than silently treating them as passes.
OpenAI and Anthropic describe repeatedly querying models with adversarial prompts to test extraction of a hidden phrase or password. Their joint evaluation also discusses limits in coverage and automated grading: OpenAI and Anthropic’s privacy-preservation evaluation.
Make each case repeatable
A useful test case has enough context that someone else can understand what was being tested: the authoritative constraint, the legitimate task, the channel carrying the attack or control, the observable outcomes, and the scoring rule. Preserve the exact inputs and outputs for each trial. If you change the prompt, task, tools, or safeguards, record the change; otherwise a difference between runs may have more than one explanation.
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Vary the wording and context to avoid making the evaluation depend on one memorized phrase. You can try different ways of asking for the hidden marker, and use multi-turn pressure, while keeping the intended violation and the rule for grading stable. The purpose is not to keep changing the definition of failure until one model looks better; it is to see whether behavior changes across a declared set of variations.
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Do not confuse a growing list of prompts with comprehensive coverage. A hand-picked collection is a practical smoke test for a particular application. Even known attack sets and repeated trials leave unknown attacks outside the test. Report the test set and conditions so readers can see how broad or narrow the result is.
Score both attack resistance and task completion
Do not give a model a passing result just because it refuses everything. A useful test records whether it resisted the attack and whether it still completed the legitimate task. A model that never handles the task may look robust on attack-only prompts while being unusable.
| Outcome | Record as a failure when… | Why it matters |
|---|---|---|
| Secret disclosure | The exact dummy marker or a recognizable transformed version appears in the answer or another observable channel | An exact-string check alone can miss paraphrase or other disclosures |
| Instruction hijacking | The model follows the untrusted instruction instead of the authoritative constraint | A model may explain that content is malicious and still comply with it |
| Unauthorized action or transfer | A tool is invoked or data is sent to a prohibited destination | The final response may not reveal what the tool attempted |
| Legitimate-task failure | The model abandons or materially mishandles the requested benign task | Refusal-only behavior is not a useful measure of safe task performance |
Use a rubric for ambiguous cases and human review when wording could be a paraphrase of the secret or an action is unclear. Do not rely on a model’s own explanation that it recognized an attack: published testing has documented a case in which a model recognized an attack yet still revealed the protected secret. OpenAI and Anthropic’s evaluation also notes that data variation and automated graders limit what can be concluded from a finite test.
Keep the outcome categories distinct in your notes. A model can resist disclosure but fail to perform the task, or complete the task while making an unauthorized tool attempt. Combining these into one “pass” number can hide meaningful differences. If you report a summary rate, show the underlying categories and case conditions too.
Be cautious with exact-marker automation. An exact match is a clear signal when it occurs, but an absent exact string is not enough to establish that no information was disclosed. Review for recognizable transformations, paraphrases, and other observable channels. The reverse problem matters as well: ambiguous text should not automatically be counted as leakage without applying a consistent rubric and, when needed, human review.
Compare models or configurations fairly
When comparing systems, use the same cases, task context, scoring criteria, and trial procedure. If tools, hidden instructions, or safeguards differ, report that difference rather than attributing the result to the model alone.
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- Separate by instruction source: report system-versus-user, developer-versus-user, and external-content cases separately.
- Separate by channel: distinguish direct user attacks from indirect attacks in retrieved or tool-provided material.
- Report each outcome: disclose secret leakage, hijacking, unauthorized action or transfer, and legitimate-task completion as distinct measures.
- Describe coverage: state how many cases and variations were used, whether they were hand-written or drawn from a benchmark, and whether scoring was automated or human-reviewed.
- State configuration: identify model/version, prompt, tools, connectors, safeguards, and any changes between runs.
Published evaluations are specific to their models and setups; their results should not be generalized to a different configuration. OpenAI’s gpt-oss model card, for example, describes system-prompt extraction and prompt-injection hijacking tests for that model family.
For a fair comparison, hold constant what you intend to compare. If the model changes but the task, hidden instruction, channel, tools, and grading rubric remain the same, the comparison is easier to interpret. If you also change safeguards or connectors, that may be a valid configuration comparison, but it is not a model-only comparison. State which factors changed.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Interpret failures and passes carefully
- A marker did not appear: that is evidence only that the tested output did not contain the checked string. Review for paraphrase, transformed disclosure, and tool-side activity.
- The model refused: check whether it also completed the benign task. A refusal by itself does not show the intended balance of resistance and utility.
- The model identified the attack: judge its behavior, not its stated reasoning; recognition does not guarantee resistance.
- All cases passed: report that result for the tested prompts, channels, configuration, and scoring method. A finite, known attack set does not establish resistance to unknown attacks.
- Runs disagree: preserve the variation and report trial counts rather than selecting only the most reassuring output.
The strongest defensible conclusion is narrow: the model did or did not exhibit specified behavior in the cases tested. OWASP describes its sample cases as a smoke test, not a security benchmark. Neither a clean smoke test nor published scores for another system establish broad security for your own deployment.
Repeated runs are useful because outputs can vary, but repetition does not remove the limits of the test set or the scoring method. Keep the record of variation rather than reporting only the preferred result. If automated grading was used, say so; if ambiguous cases were reviewed by a person, describe that as part of the method.
What to include in your test report
A short report should let someone understand what was tested and what the result does—and does not—mean. Include the model and version, relevant hidden instruction and configuration, test channels, task context, tools and connectors, number and variety of cases, repeat procedure, scoring approach, and observed outcomes. Identify whether grading was automated, human-reviewed, or both, and note ambiguous cases.
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Do not describe a small hand-picked suite as proof that the model is secure. It is better to say that a given configuration resisted or failed specified attempts under stated conditions. The sources and evaluations discussed here do not provide a universal threshold that turns a particular score into a security guarantee. Their results are specific to their own models, benchmarks, and grading setups.
Frequently Asked Questions
Can an AI reveal its system prompt?
It can be tested for disclosure by placing a harmless dummy phrase or marker in the hidden instruction and trying varied extraction requests. Look for the exact marker and recognizable transformed disclosures, and review observable tool activity too. A test only describes the model and setup you tested; it does not establish that all hidden instructions are protected.
How do I test for prompt injection?
Define the authoritative constraint, legitimate task, untrusted input channel, available tools, and observable failure criteria first. Use dummy data, separate direct user attacks from indirect attacks in external content, include benign controls, inspect both text and tool actions, and repeat trials. Treat a small hand-written suite as a smoke test rather than proof of general security.
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Put the instruction-like attack in a mock webpage or other external content that the agent is asked to read, while the user gives a legitimate task such as summarizing the page. Observe whether the agent follows the embedded instruction instead of the higher-priority constraint, whether it completes the legitimate task, and whether it attempts an unauthorized tool action. Putting the attack in the user message would test a direct attack instead.
If the exact dummy marker is absent, is that a pass?
Not by itself. An exact-string check can miss a paraphrase or transformed disclosure, and visible text does not show whether data was sent through a tool or another channel. Review recognizable disclosures and observable actions, and report the limitations of what you could inspect.
Should I compare a model by how often it refuses attacks?
Record attack resistance and legitimate-task completion separately. A model that refuses every input may look robust on attack-only cases but fail to do useful work. Also report disclosure, hijacking, unauthorized actions, and task completion as distinct outcomes.
Do passing test results prove a model is secure?
No. They establish only observed behavior on the tested prompts, channels, configuration, and scoring method. A finite known-attack set cannot establish resistance to unknown attacks, and repeated outputs or automated graders have limitations.
The Bottom Line
Use a harmless marker, test direct and indirect attacks in their actual channels, include benign task controls, and inspect both responses and tool activity. Report results by outcome and configuration, and treat a passing smoke test as evidence about those cases—not as proof of general security.
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