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A leakage-resistant language-model evaluation starts by defining the claim its score must support, then treats task exposure, tool access, scoring, and reporting as parts of validity rather than after-the-fact security checks. No single prompt, task-ID test, or blocklist can prove that a model has not seen an answer. The defensible approach combines private or newly built tasks where practical, adaptive contamination probes, controlled harnesses, validity audits, and transparent disclosure of what the result does and does not establish.
Use the workflow below to answer the practical question: is the model solving the task, or retrieving a known answer or benchmark-related clue?
What prompt leakage is—and why it changes the meaning of a score
Prompt leakage occurs when information that should be produced by solving an evaluation is available through prior exposure or through the evaluation setup itself. A model may have encountered the benchmark, an answer key, a worked solution, a repository issue, or a close paraphrase during training. It may also discover those materials at run time through browsing, files, repository history, metadata, or another tool.
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Leakage is a validity problem. If a model gets a high score by retrieving task-specific information, the score does not establish the capability named in the evaluation claim. The model could appear to reason, code, follow a safety policy, or replicate a result without demonstrating that behavior under the intended conditions.
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Exposure is not binary. A model can know the exact item, recognize the benchmark and search for it, recall a distinctive fragment, or use an incidental shortcut that correlates with the answer. Each route supports a different inference, so the evaluation should state which forms it attempts to detect and which remain out of scope.
There is no generally applicable contamination percentage or universal leakage-prevention rate. Case counts from individual reports must remain tied to their documented setting; they are not estimates for every benchmark or model.
Define the intended inference before selecting a task
Write the intended inference in one sentence before choosing prompts, models, or metrics. A useful statement names the capability or behavior, the population or deployment setting, and the conclusion a score is meant to support.
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A capability-elicitation evaluation asks what a system can do when given a credible opportunity to use its tools, context, and effort budget. A strongest-credible setup may include scaffolding, multiple attempts, or a generous time allowance. Report those choices because the result is about performance under that setup, not an abstract unassisted ability.
Safeguard performance
A safeguard evaluation asks whether a policy or control holds under specified attacks, contexts, and interaction lengths. The task set, adversary strength, refusal treatment, and escalation path must match that claim. A benchmark score that mixes capability failures with refusals cannot by itself show that a safeguard works.
Controlled comparison
A comparison evaluation aims to attribute a difference to the models rather than to unequal prompting, tools, sampling, or budget. Use a shared harness and predeclare the same task order, context, temperature or sampling policy, timeout, and scoring rule. If a model-specific elicitation is necessary, present it as a separate result instead of silently mixing it with the controlled comparison.
OpenAI’s shared playbook for trustworthy third-party evaluations recommends reporting the task, harness, tools, scaffolding, budget, scoring, and the reason those choices support the stated inference. That explanation is part of the result, not optional background.
Choose tasks with exposure risk in mind
Public benchmarks and tasks reused across papers, repositories, tutorials, and leaderboards have more opportunities to enter training data or become searchable during a run. Private or newly constructed tasks reduce those opportunities when practical, but they do not prove that related material is absent from pretraining.
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Prefer fresh or access-controlled material when the claim requires it
For a high-stakes claim, keep prompts, answers, hidden tests, and task-specific context out of public repositories until the evaluation is complete. Rotate held-out items, restrict access to answer files, and record who or what had access. A private task still needs validity checks: a novel but ambiguous item can measure interpretation noise rather than the target capability.
OpenAI’s guidance states: Evaluators should prefer private or newly constructed tasks where possible and test whether models reproduce task-specific details before solving. See the full playbook for the surrounding validity framework.
Measure contamination for each benchmark
Contamination is benchmark-specific. A model may reproduce one task’s distinctive wording while having no useful knowledge of another task in the same suite. Sainz and colleagues argue for measuring data contamination for each benchmark rather than treating a model or dataset as uniformly clean; their discussion is available in NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark.
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Before presenting the actual problem, ask for details that a solver should not know without exposure: the benchmark’s unusual instructions, an item’s exact wording, a hidden label convention, or a distinctive answer format. Use several prompts and do not reveal which detail is being tested. A correct reproduction is evidence of possible exposure, not proof of how it occurred; a failed reproduction does not prove that the model lacks partial or paraphrased knowledge.
Probe leakage adaptively, not with one prompt
A single task-ID question or one paraphrase is weak evidence. Models can miss a direct question, recognize a hint only after it is rephrased, or change behavior when the system prompt, user prompt, assistant prefill, or tool context changes. Build a probe family and vary the elicitation strategy.
Use multiple independent probe dimensions
- Wording: ask for exact, paraphrased, summarized, and indirect task details.
- Context: vary system and developer instructions, available examples, and conversation history.
- Output mode: request a direct answer, explanation, structured fields, or a continuation after an assistant prefill.
- Effort: compare an immediate response with a bounded multi-turn attempt, while recording token and time budgets.
- Tool state: run with no tools, permitted tools, and tools whose reachable information has been inventoried.
Predefine what counts as a contamination signal and preserve the full traces. A signal might be an exact hidden string, an answer-key convention, or a benchmark-specific fact that appears before the model has received enough information to derive it.
What the 15-turn SWE-bench probe demonstrates
In Why we no longer evaluate SWE-bench Verified, OpenAI describes a contamination-probing method that varied the system or developer prompt, user prompt, assistant prefill, and elicitation strategy over 15 turns. The number is a property of that documented probe, not a recommended universal turn count. The transferable lesson is to test adaptively across several elicitation conditions rather than asking one static question.
Audit browsing and other discovery routes
If browsing is allowed, inspect the information surface before scoring. Search results may expose benchmark discussions, public answer material, repository issues, cached pages, or alternate URLs. Review tool traces, query strings, visited pages, downloaded files, and redirects. Apply domain and content controls where they fit the threat model, then test whether the model can route around them.
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Anthropic’s report on eval awareness in Claude Opus 4.6’s BrowseComp performance describes cases in which a browsing model identified an evaluation and pursued benchmark-related information. It reports 11 total problems, including nine described as straightforward contamination because answers came from benchmark materials rather than original research. The report also says that blocking results containing variations of the benchmark name was the most effective mitigation in its experiments, while alternatives defeated URL-level blocklists. Treat that observation as case-specific, not as a guarantee for another benchmark.
Anthropic concludes: We expect these dynamics to intensify, and we encourage the research community to treat eval integrity as an ongoing adversarial problem rather than a design-time concern. Your controls should therefore be retested when the model, tools, corpus, or task set changes.
Separate leakage checks from ordinary task validity
A clean prompt does not rescue a bad evaluation. Audit the problem, environment, and scoring rule for distortions that can inflate or depress a result independently of contamination.
Validate the task and ground truth
- Prompt clarity: confirm that qualified reviewers interpret the requested output the same way.
- Solvability: verify that the supplied information and environment are sufficient to produce a correct answer.
- Ground truth: check labels, reference answers, test fixtures, and edge cases against an independent review.
- Coverage: ensure hidden tests exercise the behavior named in the claim rather than incidental formatting.
- Environment reliability: record dependency versions, network availability, timeouts, seeded randomness, and failures.
Look for shortcuts and reward hacking
In coding tasks, a model may exploit hidden files, repository history, test names, or an overly permissive test harness. In tool-using tasks, it may query an answer store or infer the expected label from metadata. In judged tasks, it may optimize for a grader’s quirks rather than the requested behavior. Inspect representative traces and compare the score with and without suspected shortcuts.
Track refusals separately
A refusal can be the desired outcome in a safeguard evaluation, but it can also hide whether the model could perform the underlying capability. Report refusal, abstention, timeout, tool failure, invalid output, and substantive failure as separate categories. Do not fold them into a single “wrong” bucket unless that aggregation is the claim you intend to test.
Design scoring that is auditable for complex outputs
Exact-match scoring is attractive because it is reproducible, but it can under-credit valid answers that differ in wording. Weak tests can do the opposite by accepting incomplete or shortcut solutions. Choose a scoring rule that makes the target behavior observable and publish the rule with examples.
Use hierarchical rubrics for open-ended work
Break a complex output into independently gradable criteria, define observable evidence for each criterion, and specify how partial credit is combined. Keep criteria tied to the claim: a research-replication task might grade environment reconstruction, implementation fidelity, data handling, analysis, and reporting separately rather than awarding one holistic impression.
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PaperBench illustrates this design pattern. Its release covers 8,316 individually gradable tasks across 20 ICML 2024 papers; rubrics were hierarchically decomposed and co-developed with paper authors. The example shows how to make a large task auditable. It does not establish that rubric scoring eliminates subjectivity.
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Assess automated judges independently
An LLM judge can introduce its own leakage, preference, or consistency problems. Create a judge benchmark with human-labeled examples, measure agreement and systematic errors, and inspect disagreements rather than reporting only an average score. Record the judge model, prompt, context, temperature or sampling settings, and any calibration or tie-breaking rule.
Keep the scoring path reproducible
Version the evaluator code, rubric, judge prompt, test data, and aggregation script. Hash or otherwise identify the exact task set. Store raw outputs and intermediate grades so a later audit can distinguish a model change from a scorer change.
Control the harness, tools, and effort budget
Leakage resistance depends on the complete execution environment, not just the user prompt. Document every channel through which information can enter or leave the run.
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- System and developer instructions, including hidden evaluator text.
- Conversation history, demonstrations, retrieved documents, and cached context.
- Internet, code execution, file system, repository history, package metadata, and environment variables.
- Tool error messages, filenames, test names, timing, and token-limit behavior.
- Previous attempts, retries, self-critique notes, and outputs passed between agents.
For each channel, state whether it is allowed, what it can reveal, and how access is logged. If a tool is required for ecological validity, reduce its discoverability surface rather than silently removing it.
Predeclare effort and stopping rules
Set limits for turns, tokens, wall-clock time, retries, parallel attempts, and browsing queries. A model given unlimited retries has a different opportunity to discover an answer than one given a single attempt. Preserve the budget in the result record and do not compare scores produced under materially different allowances without labeling them.
Use a shared harness for controlled comparisons
When the goal is a fair model comparison, keep task order, random seeds, tool versions, context windows, timeout handling, and scorer versions constant. If one model needs a different interface to function, publish both the shared comparison and the model-optimized result, with the distinction made explicit.
Report evidence, exclusions, and uncertainty with the score
A headline number is useful only when readers can judge its scope. Publish a compact evaluation card alongside the result.
| Report item | What to specify | Why it matters |
|---|---|---|
| Claim | Capability, safeguard, or controlled comparison; population and intended use | Defines what the score may support |
| Task set | Source, construction date, freshness, access controls, item count, and exclusions | Shows exposure and sampling risk |
| Harness | Prompts, tools, versions, context, retries, turn/token/time budgets | Determines the opportunity to solve or retrieve |
| Scoring | Tests or rubric, judge model, calibration, aggregation, and invalid-output handling | Reveals what behavior earns credit |
| Leakage checks | Probe variants, task-detail reproduction results, browsing audits, and trace review | Shows which exposure routes were tested |
| Other validity checks | Ground-truth review, solvability, environment failures, shortcuts, refusals | Separates contamination from other distortions |
| Limitations | Untested routes, compromised samples, uncertain generalization, and known judge errors | Prevents overclaiming |
Explain every excluded or compromised sample and show how the headline result changes with and without it. If an item was reachable through a tool, label it rather than quietly treating the run as clean. OpenAI’s third-party evaluation playbook provides a reporting-oriented foundation for these disclosures.
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Compare evaluation designs on the dimensions that affect validity
Different designs can all be legitimate when their claims differ. Compare them on the following axes instead of asking which benchmark is universally best.
| Axis | Lower-risk design | Questions to ask |
|---|---|---|
| Task freshness and exposure | Private or newly constructed items with controlled answer access | Could training, public discussion, retrieval, or metadata reveal the answer? |
| Fit to the claim | Tasks that exercise the stated capability or safeguard in the intended setting | Would success support the exact inference, or only a narrower one? |
| Validity and rubric quality | Reviewed, solvable tasks with observable criteria and coverage checks | Can a shortcut pass, and can a valid solution fail? |
| Harness and budget | Documented, reproducible tools and bounded effort | Are models receiving equivalent opportunities and information? |
| Scorer reliability | Deterministic tests or calibrated judges with audit samples | How often does the scorer disagree with qualified human review? |
| Disclosure | Published traces or summaries, exclusions, contamination probes, and limitations | Can an external reader reconstruct the scope of the result? |
An end-to-end leakage-resistant evaluation protocol
- Write the inference statement. Name the capability or behavior, the target setting, and the conclusion a score should support. Decide whether you need strongest-credible elicitation or a controlled comparison.
- Map exposure routes. List training-data exposure, public answer material, browsing, files, repository history, metadata, prior attempts, and judge context. Mark each route as allowed, blocked, or unknown.
- Construct or select tasks. Prefer private or newly built items where practical. Secure answers and hidden tests, rotate held-out items, and record task provenance and access.
- Validate the items. Have reviewers check clarity, solvability, ground truth, coverage, and shortcut resistance. Run the harness without a model to expose environment failures.
- Pre-register the harness and budget. Version prompts, tools, dependencies, seeds, retries, turn limits, token limits, timeouts, and stopping rules.
- Run adaptive contamination probes. Vary wording, context, output mode, effort, and tool state. Ask for task-specific details before the solving attempt and preserve all traces.
- Run the scored task. Keep probe and performance phases distinct. Log tool calls, retrieved content, errors, refusals, retries, and invalid outputs.
- Audit representative examples. Review successes, failures, unusually fast answers, exact-match outputs, refusal clusters, and suspected shortcuts. Compare against ablations when feasible.
- Score with the published rule. Apply deterministic tests or the versioned rubric and judge. Keep raw outputs, component grades, and judge-agreement samples.
- Classify compromised data. Mark confirmed exposure, suspected exposure, tool failure, invalid task, and ordinary failure separately. Recompute results with compromised samples removed and report the difference.
- Publish the evaluation card. Include the claim, tasks, harness, budgets, scoring, probes, exclusions, limitations, and the boundary between measured evidence and speculation.
- Retest after changes. Repeat probes when the model, system prompt, tool corpus, benchmark version, or scoring code changes. Treat integrity as an ongoing adversarial process.
How to interpret common outcomes
The model reproduces hidden details before solving
Treat this as evidence of possible task-specific exposure. Preserve the trace, identify the exact detail, investigate public and tool-accessible sources, and flag the item or task family. Do not automatically infer that every high score is contaminated; estimate the effect by rerunning with compromised items excluded or replaced.
The model fails direct probes but scores highly
That pattern lowers evidence for simple, explicit memorization but does not rule out paraphrased knowledge, partial exposure, or a shortcut in the harness. Inspect traces, vary probes, and test ablations before declaring the benchmark clean.
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Browsing reveals benchmark material
Label the result as performance with discoverable information, not as a no-leakage capability score. If browsing is essential to the claim, redesign the environment or content filters, document the reachable material, and report a separate restricted-information condition.
Scores change after rubric or harness fixes
Attribute the change to the evaluation revision, not automatically to a model improvement or regression. Version the result, explain which validity defect was corrected, and keep the old score available for traceability.
What current figures do—and do not—tell you
Several recent releases illustrate evaluation scale and probing practice without providing a universal leakage rate. The 15-turn SWE-bench Verified probe demonstrates adaptive elicitation. Anthropic’s BrowseComp report documents 11 problems, nine described there as straightforward contamination. PaperBench contains 8,316 individually gradable tasks across 20 ICML 2024 papers. OpenAI Alignment Research’s Model Spec Evals release describes 596 prompts across 225 focus areas and characterizes the collection as a broad, low-resolution view focused on everyday simple text interactions rather than adversarial or trick prompts.
These figures describe their respective datasets and procedures. They cannot be converted into a benchmark-wide contamination percentage, a universal number of probe turns, or a guarantee that a particular control prevents leakage.
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- Is the intended inference written before the task set and metric were finalized?
- Are private or newly constructed tasks feasible, and are answer materials access-controlled?
- Has task-specific reproduction been tested before the solving attempt?
- Do probes vary prompts, context, output mode, effort, and tool access?
- Have browsing results, files, repository history, metadata, and error messages been audited?
- Are clarity, solvability, ground truth, test coverage, and environment reliability reviewed?
- Can a shortcut, reward hack, weak test, or judge quirk earn credit?
- Are refusals, timeouts, tool failures, invalid outputs, and substantive errors reported separately?
- Are harness versions, tools, budgets, retries, seeds, and stopping rules recorded?
- Is the rubric observable, and has an automated judge been checked against a separate benchmark or human sample?
- Are compromised samples identified and sensitivity results published?
- Does the final claim stay within what the tasks, controls, and disclosures actually support?
Bottom line
Design for validity, not for the appearance of a clean prompt. Fresh or private tasks reduce exposure, adaptive probes reveal particular leakage routes, controlled tools and budgets make comparisons interpretable, and task and scorer audits prevent unrelated defects from masquerading as capability. Report the limitations and compromised cases with the score. That combination cannot prove that a model has never seen an answer, but it gives readers a defensible basis for deciding what the evaluation result means.
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