The Tool Desk
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Decide what the evaluation is meant to establish
An evaluation is a structured, controlled measurement of a defined property. The UK AI Safety Institute (AISI) says its evaluations are not comprehensive safety assessments and are not intended to designate a system “safe.” A result is evidence about the tested model, tasks, and conditions—not a certificate covering every use. See the UK AISI approach to evaluations.
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Before testing, name the decision the results will inform and choose the assessment target. These three targets overlap in practice, but they are not interchangeable:
- Cyber capability: what the model can do on defined tasks, with stated tools, access, and attempt limits. A capability may support defensive work or be misused; a benchmark score alone does not establish real-world impact.
- Safeguards: whether specified protections work against the threats and failure modes in scope. A few refusals on test prompts do not establish that safeguards are robust.
- Deployment security: whether the complete system—including APIs, tools, data pipelines, permissions, and operational controls—is secure enough for its intended context. Testing a base model does not assess these surrounding components.
Write down the authorized environment, boundaries, assumptions, relevant threat actors, access level, tools, and intended defensive use. Avoid collapsing the outcome into a single “cyber score”: a high-level aggregate can hide important differences between tasks and safeguards.
#1 Best Overall
Build a threat-informed test plan
Select tasks because they represent a threat, defensive need, or safeguard requirement that matters to your decision. State why each is in scope and which part of the cyber workflow it tests. Consider AI-specific risks where relevant, including data poisoning, model inversion, and membership inference, as well as the cyber tasks associated with the intended use.
Plan capability tests and safeguard tests separately. For safeguards, convert policy language into concrete, testable requirements: specify the behavior expected, the conditions under which it should hold, and what evidence would count as failure. Set test boundaries and stop conditions in advance; define who monitors execution and what incident-response and recovery steps apply if a run leaves scope.
Control the model artifact and test environment
Treat model weights and related assets as security-sensitive objects. Open-weight availability does not remove the need to manage provenance, integrity, access, or changes. Record enough detail for another evaluator to understand what was actually tested:
- Model name, source, exact revision or hash where available, and date obtained.
- Any quantization, fine-tuning, conversion, or other transformation applied to the artifact.
- Inference settings, system prompt, harness, evaluator, and tool configuration.
- Evaluation data, credentials, logs, and environment state needed to reproduce or interpret results.
Restrict access to weights, data, logs, and credentials according to least privilege. Verify hashes where available, document provenance and material changes, and include APIs or pipelines in scope when they are part of the deployment being assessed. The UK Government’s Code of Practice for the Cyber Security of AI calls for security assessment of released models, applications, and systems; its guidance also covers inventories, protections for potentially confidential weights, and documented changes.
Run staged tests with containment
Start with baseline tasks to establish expected behavior under controlled conditions. Use what those runs reveal to select more focused tests, then add expert red-teaming when the risk, intended use, or initial findings justify it. Keep test inputs, attempt budgets, and allowed resources consistent enough to make comparisons meaningful.
Any task involving untrusted code or potentially dangerous agent actions belongs in an isolated execution environment. Minimize external connectivity and permissions to what the test requires. Do not give a model access to production systems, real secrets, or unrestricted tools simply to make a task more realistic; reproduce relevant conditions in a controlled range where possible. Monitor execution against the pre-set stop conditions.
Rank #3
Capture the whole trajectory and make scoring interpretable
For an agentic cyber task, the final answer is only part of the result. Record the prompts and other inputs, model responses, tool calls, execution outputs, number of attempts, relevant settings, and environment state. This trajectory can show whether apparent success depended on a tool, an intermediate action, or a particular setup—and can expose failures hidden by a final-response-only score.
Define scoring criteria before interpreting outcomes. Document whether scores are produced automatically, with model assistance, or by human judges, and preserve the expected outputs or task-specific criteria used. Include the baseline and attempt budget: results can change with the task set, tools, scaffolding, context, number of attempts, and time limits. A score from one suite does not generalize to all cyber work or to all open-weight models.
What published cyber benchmark results can tell you
A December 2024 joint report by the US and UK AI Safety Institutes illustrates why a result needs its test context. The report evaluated OpenAI o1—not open-weight models—on two different cyber task sets:
| Evaluation | Task set | Reported result |
|---|---|---|
| US AISI Cybench evaluation, 2024 | 40 challenges drawn from public capture-the-flag competitions | Estimated Pass@10 was 45% for o1, compared with 35% for the best evaluated reference model. |
| UK AISI evaluation, 2024 | 47 challenges: 15 publicly sourced and 32 privately developed | Pass@10 was 79% for o1 on technical-non-expert tasks, compared with 90% for the best reference model. |
| UK AISI evaluation, 2024 | The same 47-challenge UK suite | Pass@10 was 46% for o1 on cybersecurity-apprentice tasks, matching the best reference model’s 46%. |
These figures describe results under those report-specific tasks and conditions. The report characterizes the tested tasks as a relatively narrow slice of cyber activity and identifies needs including broader task coverage, more realistic challenges, human baselines, expert-operator interaction, and stronger comparisons of task time and attempts. They are not evidence of how an open-weight model will perform, or what impact it will have, in a real deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate safeguards as evidence-backed claims
For each safeguard requirement, identify the evidence needed and the threats it is intended to address. Depending on the claim, evidence may include appropriately scoped red-teaming, static tests on existing datasets, or robustness evaluations. Independent third-party collection or assessment can be useful where appropriate, particularly when the evaluator’s own incentives or access could affect confidence in the result.
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Keep safeguard findings distinct from capability results: demonstrating that a model can perform a task does not show that its safeguards work, and observing a refusal on a small prompt set does not establish that they will hold across different contexts, tools, or attempts. Reassess after deployment as well as after model changes or newly observed attacks.
Best Value
Report results with their limits
A useful report lets another team judge both the result and its relevance. Include the tested model revision and configuration, task sources and coverage, number of tasks and attempts, tools and environment, scoring method, baselines, outcomes, observed failures, and limitations. Separate benchmark performance in a controlled setup from claims about likely attacker impact or operational effectiveness.
State what was outside scope and communicate known limitations and failure modes to operators and users who may rely on the findings. Treat a material model update as a new version for evaluation purposes: repeat relevant tests rather than assuming earlier results carry over. The UK Government’s AI cyber security code likewise calls for testing models, applications, and systems as part of a security assessment process.
How to compare evaluation approaches
When choosing a benchmark, cyber range, or independent evaluator, compare the method with your intended use rather than choosing by headline score. Check whether it:
- Covers the threats and cyber task phases in your threat model, with realistic and appropriately difficult tasks.
- Provides tasks and procedures that are reproducible or sufficiently documented to interpret.
- Matches the tools, internet access, agent scaffolding, and access level of the use case—or clearly explains the difference.
- Specifies attempt budgets, time limits, and costs so results can be compared fairly.
- Uses reliable scoring and meaningful human baselines, and accounts for expert-operator interaction when relevant.
- Provides isolation and monitoring for code execution or agent actions.
- Tests safeguards and deployed-system behavior separately from base-model capability where those questions matter.
No single approach answers every question. Choose a suite for its coverage and controls, then state which important parts of your threat model it does not test.
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