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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Hidden system prompts can affect what a model is told, how it answers, and whether its output satisfies a benchmark’s scoring rules. If those instructions differ between models—or quietly supply context aligned with test questions—a reported score may measure the prompt condition as well as the capability the benchmark is meant to assess. The size of that effect is not established: the studies discussed here document benchmark-data contamination and prompt leakage, but do not calculate a general causal adjustment to benchmark scores.
What is the risk to a benchmark score?
A benchmark score is an outcome of an evaluation setup, not an isolated reading of a model’s underlying ability. The model receives inputs, generates responses, and those responses are judged by a scoring procedure. System instructions can influence any response produced in that setup. If the instructions are hidden or differ across compared models, readers may not have enough information to tell what the score represents.
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For example, an instruction might add task-specific context, require a particular response format, restrict the kinds of answers the model may give, or encourage vocabulary that overlaps with a grader’s rubric. Such effects are plausible ways prompt conditions could change answers or scoring; they are a causal explanation, not a measured effect size established by the studies cited here. The consequences depend on the prompt, benchmark, model, and scoring method.
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This matters most when a result is presented as a comparison of model capabilities. If one model is evaluated with materially more helpful context, different output constraints, or a rubric-aligned instruction, a higher score cannot automatically be attributed to the model alone. A prompt can be part of the treatment being evaluated, whether or not the report identifies it that way.
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How do hidden instructions influence the evaluation?
They can change the information available at inference time
A system prompt can supply context before a benchmark question is answered. If that context contains knowledge, definitions, or task guidance relevant to the test, the model is not answering under the same information conditions as a model that did not receive it. The score then reflects performance with that additional context. This is not necessarily improper: some evaluations intentionally test systems configured with instructions. It becomes a comparability problem when the condition is undisclosed or inconsistent with the claim being made.
They can shape response form as well as content
Instructions may require concise answers, a specific schema, a refusal in defined cases, or a particular reasoning or formatting style. Those constraints can affect whether an answer is accepted by exact-match scoring, a programmatic checker, or a human or model grader using a rubric. A correct response in an unanticipated format may receive no credit from a strict checker; conversely, a rubric that rewards certain wording may favor an instruction that elicits that wording. The evaluator should distinguish a capability failure from a mismatch between the response and the scoring procedure.
They can create unequal experimental conditions
When two models receive different instructions, their scores compare both the models and the prompt setups. Even apparently small differences—extra context, a changed output format, or a different definition of success—may matter for some tasks. A controlled comparison should therefore treat prompt text and hidden context as evaluation conditions, not as incidental implementation details.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesPrompt effects, prompt leakage, and data contamination are different
These terms describe related but distinct problems. Keeping them separate prevents evidence about one from being mistakenly presented as proof of another.
- Inference-time prompt condition: the instructions and context supplied to a model during the evaluation. These can alter the response produced for a benchmark item.
- Prompt leakage: an application reveals some of its hidden instructions or context through its output, often in response to probing. Leakage concerns whether the prompt can be extracted; it does not by itself establish that benchmark scores changed.
- Training-data contamination: benchmark questions, answers, or related material may have appeared in a model’s training data. That is a possible source of prior exposure, separate from instructions supplied during the test.
A model’s ability to recover benchmark-related material is evidence relevant to possible contamination, not a direct measurement of score inflation. Likewise, a successful prompt-extraction attack says something about disclosure risk under that attack setup, not how many points a benchmark score gained. Neither finding supplies a universal correction factor.
What do the studies actually show?
Benchmark-data exposure: the NAACL 2024 study
Chunyuan Deng and coauthors’ NAACL 2024 paper examines contamination in modern LLM benchmarks. Among its approaches, it introduces Testset Slot Guessing: mask a wrong answer or an unlikely word from a test example and check whether a model can supply the missing item. In the paper’s MMLU study, exact matching of missing options was reported for 52% of cases for ChatGPT and 57% for GPT-4. Read the NAACL 2024 study.
Those figures are study-specific signals that benchmark-related content may be recoverable. They are not estimates that ordinary ChatGPT or GPT-4 benchmark scores were inflated by 52% or 57%, and they do not measure the effect of a hidden system prompt. The paper addresses possible data contamination, a different issue from inference-time prompt conditions.
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System-prompt extraction: the EMNLP Industry Track 2024 study
Divyansh Agarwal and coauthors studied prompt leakage across 10 closed- and open-source models and four domains. Under the study’s multi-turn threat model, average attack success rate rose from 17.7% to 86.2%. The result concerns the success of prompt-extraction attacks under the authors’ setup; it is not a benchmark-score effect and should not be read as a general probability that any prompt will leak. Read the EMNLP Industry Track 2024 study.
Deployed applications: a 2026 preprint
A 2026 arXiv preprint reports that over 80% of 1,200 applications across six commercial platforms leaked system prompts under the authors’ realistic adversarial queries. This is a finding from the paper’s tested sample and attack conditions, not a universal rate for all deployed LLM applications. It addresses prompt leakage, not the magnitude of distortion in benchmark scores. As a preprint, it should be identified as such rather than treated as a settled industry-wide measurement. Read the 2026 preprint.
Security guidance: OWASP’s 2025 framing
OWASP treats system-prompt leakage as a security concern, noting that prompts may expose sensitive information and warning against relying on prompt secrecy as an access-control mechanism. For evaluators, the related methodological lesson is that prompt text and other hidden context can matter enough to document and control. Security guidance does not itself quantify an effect on benchmark scores. Read OWASP’s 2025 LLM Top 10 entry on system prompt leakage.
Are benchmark results comparable when prompts differ?
Not without qualification. A score comparison is easier to interpret when the compared systems were evaluated under equivalent prompt conditions and scoring rules. If prompts differ, the result may still be useful, but it answers a different question: how the tested model-plus-prompt configurations performed, rather than how the models compare under a shared instruction condition.
Use the distinctions below when reading or designing an evaluation:
| Evaluation dimension | Why it affects interpretation | What to check |
|---|---|---|
| Prompt transparency and reproducibility | Hidden instructions can shape answers, while undisclosed details make it difficult to reproduce the condition. | Is the system prompt available, or is its relevant content described sufficiently to understand and reproduce the evaluation? |
| Prompt consistency across models | Different instructions can turn a model comparison into a comparison of configurations. | Did each model receive equivalent task instructions, context, and output requirements? |
| Benchmark exposure checks | Possible training-data overlap is separate from prompt effects, but can also complicate claims about capability. | Were contamination probes such as retrieval overlap analysis or slot-guessing used, and what exactly do their results establish? |
| Scoring and rubric sensitivity | Output constraints and wording may affect whether a response receives credit. | Is scoring exact-match, programmatic, human-judged, or model-graded, and could its rules favor a particular response style? |
| Attack or evaluation setting | Prompt extraction under one interaction pattern does not establish behavior in another. | Were the results obtained in a single-turn or multi-turn setting, and what threat model was tested? |
How should evaluators report prompt conditions?
No universal prompt-reporting standard or numerical adjustment factor is established by the sources discussed here. The following practices are methodological recommendations for making comparisons interpretable, rather than a claim that one prescribed standard has been adopted across the field.
- Publish the prompt when possible. Include the system instructions and relevant hidden context, or provide a precise description of the parts that cannot be released. Explain what is omitted and why, so readers can assess whether the restriction could affect task performance.
- Keep conditions equivalent. For model comparisons, use the same task instructions, context, output format, and scoring setup wherever the systems allow it. If a model requires a different wrapper or prompt, document that difference instead of implying a perfectly matched test.
- Separate prompt variants. If the study tests different instructions, report each as a distinct evaluation condition. Do not combine their results in a way that obscures which prompt produced which score.
- Describe scoring in operational terms. State how answers earn credit, including exact-match normalization, parser behavior, human-judge instructions, or model-grader rubrics as applicable. Identify response-format requirements that could determine whether an otherwise correct answer is accepted.
- Describe contamination checks separately. If overlap analysis or slot-guessing is used, report the method and result as evidence about possible exposure. Do not label it a measurement of prompt-induced score change.
- Make the claim match the condition. A result from a configured application supports a claim about that tested configuration. A claim about a model’s performance under a common prompt requires an evaluation designed to hold prompt conditions steady.
What would establish whether a prompt changed the score?
A direct estimate requires a controlled comparison: vary only the prompt condition while holding the model, benchmark items, inference settings, and scoring procedure steady. For example, an evaluator could score the same model on the same item set under a disclosed baseline instruction and under a specified alternative instruction, then report the results as separate conditions. Repeating that comparison across models or benchmarks could show whether the effect is limited to particular tasks or more consistent.
That design would estimate the effect of those tested prompt changes in that evaluation. It would not automatically establish a universal effect for all hidden prompts, benchmarks, or scoring systems. A useful report would make the compared prompts available, state the exact benchmark and scoring rules, and identify any remaining differences between conditions. The sources described above do not supply such a general causal estimate, nor do they determine which scoring methods are most robust to hidden-prompt effects.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallHow should readers interpret a reported score?
Before treating a score as a clean measure of model capability, check what the evaluation actually fixes and reveals. Ask whether the prompt was disclosed, whether all compared models received equivalent context and output constraints, and whether the scoring procedure rewards a style the prompt may have encouraged. Then keep any contamination finding in its proper category: evidence of possible prior exposure is not proof of a prompt effect or a numerical score correction.
If those details are missing, the result is not necessarily invalid. It is less reproducible and supports a narrower conclusion: the tested system achieved the reported score under conditions that are not fully specified. The more a paper’s conclusion depends on attributing differences to the model itself, the more important prompt transparency and condition matching become.
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