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Hidden system prompts can affect benchmark answers by adding context, shaping how a model interprets a task, or constraining the form of its response. If those instructions differ between models—or quietly supply information relevant to the test—a score may measure both model capability and prompt conditions. The size and even the direction of that effect depend on the benchmark and scoring method; the available studies do not establish a general score correction or a universal amount of distortion.
What does a hidden system prompt change in an evaluation?
A system prompt is instruction or context supplied to a model outside the visible benchmark question. An evaluator may see only the test item and the answer, while the model also receives a hidden instruction that sets a role, gives task-specific context, limits what it may say, or specifies an answer format. The model’s output is therefore produced under a larger input condition than the visible question alone.
This matters when the intended claim is that one model performs better at a particular capability. If two models receive materially different hidden instructions, their scores do not isolate model capability: they also reflect the conditions under which each was tested. Even with the same underlying model, changing the system prompt can change what information is available, how a question is framed, or whether the answer follows a scoring-friendly format. These are plausible mechanisms for confounding, not a measured universal effect established by the studies discussed below.
The effect need not be positive. A prompt could supply useful context or clarify the task; it could also restrict a valid answer, steer the model away from the expected response, or require a format that the scorer handles poorly. If the prompt has no bearing on the benchmark items or scoring, it may make little difference. The relevant question is not simply whether a hidden prompt exists, but whether its content interacts with the tested task and scoring procedure.
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How can prompt differences alter a benchmark score?
Information and task context
An instruction can provide facts, terminology, or task-specific guidance that helps answer a test item. If that material overlaps with benchmark content, a high result may partly reflect the supplied context rather than the capability the evaluator intends to measure. The same issue arises if the prompt points the model toward an interpretation or strategy that is especially useful for the test.
Task framing and response constraints
Prompts can define what counts as a good answer, request a particular role or reasoning approach, or restrict the model to certain kinds of responses. That framing can affect the answer even when it does not reveal a test answer outright. A restriction can suppress a response that would otherwise receive credit; a clear instruction can make the desired response easier to produce. Thus, identical questions are not necessarily equivalent evaluation conditions when the surrounding instructions differ.
Output format and scoring
A hidden instruction may require a label, JSON-like structure, a short answer, or another format. A human grader may understand a response that an exact-match scorer rejects. Conversely, a requested format may make answers easier for an automated scorer to parse. For model-graded evaluations, wording and style can also matter if the rubric rewards particular features. These possibilities make the scoring rule part of the prompt-confound analysis, not a separate afterthought.
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These mechanisms are a reasoned methodological synthesis, not results directly quantified by the cited prompt-leakage or contamination papers. The sources establish that benchmark-related information can be recoverable and that system prompts can be exposed; they do not measure how much hidden instructions change scores in a general benchmark comparison.
How are hidden-prompt effects different from contamination and prompt leakage?
Three related concerns are easy to conflate, but they refer to different stages of an evaluation or application:
- Inference-time prompt condition: instructions or context given to a model while it is taking the evaluation. This can affect the answer even if the prompt remains secret and no attack occurs.
- Prompt leakage: a model or application reveals some of its hidden instructions or context in its output. Leakage is a disclosure risk; it is not itself proof that benchmark answers were improved.
- Training-data contamination: benchmark questions, answers, or related material may have appeared in a model’s training data. This concerns what the model may have learned before evaluation, rather than what it receives in a system prompt at evaluation time.
The risks can coexist, but evidence for one does not establish another. For example, finding that a model can recover benchmark-related content is a reason to investigate possible exposure; it does not show that a hidden system prompt caused a score increase. Likewise, demonstrating that an application can leak its instructions does not quantify the effect of those instructions on a benchmark.
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What do the published studies show—and what do their numbers mean?
| Study and scope | Reported result | What it supports | What it does not establish |
|---|---|---|---|
| Deng et al., NAACL 2024; benchmark contamination analysis | In a Testset Slot Guessing study on MMLU, missing-option exact matching was reported at 52% for ChatGPT and 57% for GPT-4. | Benchmark-related content may be recoverable by models, making contamination checks relevant. | These rates are not estimates that ordinary benchmark scores were inflated by 52% or 57%, and they do not isolate hidden system-prompt effects. Read the NAACL paper. |
| Agarwal et al., EMNLP 2024 Industry Track; prompt leakage in multi-turn applications | The study examined 10 closed- and open-source models across four domains. Its multi-turn threat model raised average attack success rate from 17.7% to 86.2%. | Prompt extraction can be a substantial risk in the tested multi-turn application setup. | Attack success rates are not benchmark-score effects, nor should the result be treated as a leakage rate for all models or evaluation settings. Read the EMNLP Industry Track paper. |
| 2026 arXiv preprint; tested real-world LLM-based applications | Among 1,200 applications across six commercial platforms, the authors report that over 80% leaked system prompts under their realistic adversarial queries. | The preprint reports widespread leakage within its tested sample and query conditions. | This is a preprint result tied to the stated sample and platform scope, not a universal industry rate or a measure of benchmark inflation. Read the preprint. |
Deng et al. introduced Testset Slot Guessing, which masks a wrong answer or an unlikely word and checks whether a model can supply the missing material. Their paper also proposes retrieval-based overlap analysis for investigating possible data contamination. The MMLU exact-match figures are signals of possible exposure under that study’s method; they should not be transferred directly into claims about score inflation or about the effect of system prompts.
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The prompt-leakage studies answer a different question: whether hidden instructions can be extracted from applications under tested attack conditions. Agarwal et al.’s multi-turn result describes the effect of a threat model on attack success in their study, not a change in benchmark accuracy. The 2026 finding is explicitly a preprint result, with the sample of 1,200 applications across six commercial platforms and the adversarial-query condition attached to it. Neither finding supplies a causal estimate for how scores change when system prompts are varied.
OWASP’s 2025 guidance treats system-prompt leakage as a security concern. It warns that prompts may expose sensitive information and that access control should not rely on keeping prompt text secret. For evaluation practice, that is a reason to treat hidden context as an experimental condition to control or disclose—not a reason to assume leakage has already altered a benchmark result. See OWASP LLM07: 2025 System Prompt Leakage.
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How can evaluators test whether prompts distort a result?
A useful test estimates a prompt effect by changing the prompt condition while holding other parts of the evaluation steady. The aim is to separate variation attributable to the instruction from variation attributable to the model, test items, scoring, or execution setup. The cited sources do not prescribe one universal protocol or correction factor; the following steps are practical experimental recommendations based on the confounds described above.
- Define the capability and comparison. State what the benchmark score is intended to measure and which models or prompt conditions are being compared. Specify whether the question is about a model under a particular product configuration or the model’s capability under a shared instruction.
- Record the complete prompt condition. Preserve the system instruction and relevant hidden context, along with the visible task instructions and required output format. If an application cannot disclose the full prompt, provide enough detail about relevant instructions for readers to understand and reproduce the condition where possible.
- Hold conditions constant across models. Use the same relevant instructions, context, task wording, and output requirements for each compared model, or explain why a difference is necessary. A comparison that uses different prompts should report those as different conditions rather than treating them as an invisible implementation detail.
- Vary one prompt factor at a time when estimating its effect. Compare a baseline with a defined prompt variant while keeping the benchmark items, model version, decoding or execution settings, and scoring procedure fixed. If multiple factors change together, the result cannot show which change drove any score difference.
- Check benchmark exposure separately. Use methods such as retrieval-based overlap analysis or slot-guessing checks to investigate possible training-data contamination. Report these as exposure checks, not as estimates of the system-prompt effect.
- Audit how answers earn credit. Describe whether scoring uses exact matching, another automated rule, a human grader, or a model-based rubric. Check whether formatting constraints or rubric wording could reward one response style independently of the underlying capability.
- Report results by condition. Show scores for each prompt variant and the scoring method used. Avoid collapsing materially different instructions into a single headline number without explaining the aggregation.
When prompt variants are part of the research question, they are experimental conditions in their own right. Reporting their results separately makes it possible to see whether a conclusion is stable across instructions or depends on a particular framing. It also avoids presenting a prompt-specific outcome as though it were an unqualified property of the model.
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A reader needs enough detail to judge what was tested and whether another evaluator could reproduce the relevant conditions. These reporting dimensions serve different purposes and should not be collapsed into a generic statement that “prompts were standardized.”
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| Reporting dimension | What to disclose | Why it matters |
|---|---|---|
| Prompt transparency and reproducibility | The system prompt or a sufficiently detailed account of relevant instructions and hidden context. | Readers cannot assess prompt-specific information or constraints if they do not know what the model received. |
| Prompt consistency | Whether compared models received the same relevant instructions, context, and output requirements; identify and explain differences. | Prompt differences can become differences in experimental conditions. |
| Prompt variants | Each tested variant and its result, rather than only a pooled result where conditions materially differ. | Separate reporting shows whether findings are robust to framing or depend on one instruction. |
| Benchmark exposure checks | Methods used to probe possible overlap or recoverable test content, with their scope. | Exposure analysis concerns possible contamination and should remain distinguishable from inference-time prompt effects. |
| Scoring and rubric sensitivity | The scoring rule, grader type, output-format requirements, and any rubric features likely to influence credit. | Instructions may affect whether a response is accepted even when its substantive content is similar. |
| Attack or evaluation setting | Whether a prompt-leakage result uses single-turn or multi-turn interactions and the tested threat conditions. | Leakage findings from different setups are not interchangeable and do not directly quantify score effects. |
OWASP’s security point has a practical bearing here: a benchmark should not treat secrecy as a substitute for controlling access to sensitive information. Evaluators can disclose prompt conditions for scientific interpretability while avoiding the assumption that a prompt itself is a secure boundary. If a full prompt contains sensitive material that cannot be published, the report should still describe the relevant evaluation condition as precisely as possible without exposing that material.
Can a reported score be corrected for hidden prompts?
There is no general numerical adjustment supported by the cited evidence. The actual effect would depend on the prompt contents, the benchmark questions, the model, and how answers are scored. A leakage percentage, a missing-option guessing rate, or an application prompt-extraction success rate cannot be converted into a benchmark-score correction: each measures a different outcome under a different setup.
A defensible estimate for a particular evaluation requires controlled comparison. Vary the prompt condition, keep the remaining evaluation elements steady, and report the resulting scores and scoring details. If those conditions were not controlled or disclosed, readers can identify a comparability limitation, but they cannot infer the size or direction of the prompt effect from the reported score alone.
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