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Safely evaluating an open-weight AI model’s cybersecurity capabilities starts with a written threat model, an authorized test scope, and the exact model artifact and configuration you intend to assess. Run tests in a controlled environment, isolate untrusted code and risky tool actions, record the full run—not just the final answer—and report what the evaluation does and does not establish. A benchmark result is evidence about particular tasks under particular conditions, not a general safety certificate.
Decide what you are evaluating
“Cybersecurity capability” can mean several different things. A model’s ability to solve a security task, its safeguards against harmful use, and the security of the system around it are separate questions. Define which one your evaluation is meant to answer before selecting tests; one aggregate “cyber score” cannot stand in for all three.
| Evaluation target | Question to answer | Evidence to collect |
|---|---|---|
| Model capability | How does this model perform on the specified cybersecurity tasks? | Task-level results under documented prompts, tools, attempt limits, and scoring rules. |
| Safeguards | Does the system meet explicit requirements for the threats and use cases in scope? | Evidence from scoped red-teaming, static tests on existing datasets, or robustness evaluations. |
| Deployment security | Are the model’s APIs, pipelines, assets, and permissions protected in the tested environment? | Assessment of the relevant system components, access controls, inventories, and changes. |
The UK AI Safety Institute defines evaluation as “a structured, controlled process for measuring a property of an AI system.” It also cautions that its evaluations are not comprehensive safety assessments and are not intended to designate a system “safe.” See the UK AI Safety Institute approach to evaluations.
Write a threat-informed test plan
State the decision the evaluation will inform, the defensive use case, the authorized environment, and the boundaries of testing. Identify relevant threat actors, assumptions, access levels, tools, and task phases. For each proposed task, record why it is relevant to the threat model rather than treating a broad collection of cyber challenges as inherently representative.
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- Specify whether you are measuring capability, safeguards, deployment security, or a defined combination.
- Set the model artifact and version, inference configuration, tools, and agent scaffolding that are in scope.
- Define allowed systems and actions, stop conditions, monitoring, incident response, and recovery before testing begins.
- Consider AI-specific security concerns where relevant, including data poisoning, model inversion, and membership inference; the UK Government’s Code of Practice for the Cyber Security of AI calls for threat modeling and regular review.
Capability and safeguards may interact, but they need separate claims and evidence. A model’s ability to help with a defensive task does not by itself establish that safeguards are adequate, and a refusal on a small set of prompts does not prove safeguards work against the threats in scope.
Protect and identify the model artifact
Open weights are security-relevant assets. Record enough provenance and configuration detail for another evaluator to understand what was tested and, where possible, reproduce it. The UK Government’s AI Cyber Security Code calls for model and asset inventories, protection for potentially confidential weights, cryptographic hashes for shared model components, and documented changes. Its principle 9.1 says developers and system operators should ensure released models, applications, and systems have been tested as part of a security assessment process.
- Record the model name, source, exact revision or hash, and evaluation date.
- Document quantization or other transformations, inference settings, system prompt, harness, tools, and evaluator.
- Protect weights, evaluation data, logs, and credentials; grant only the access needed for the test.
- If a deployment is in scope, include its relevant APIs and pipelines rather than assuming a base-model test covers them.
Run tests in controlled stages
Begin with baseline tasks, use their results to select focused tests, and add expert red-teaming where the risks and decision justify it. The UK AI Safety Institute’s Evaluation Framework Playbook treats task samples, solvers, and scorers as core elements of an evaluation. Its guidance also supports sandboxed execution when testing involves untrusted code or potentially dangerous agent actions.
- Set up containment. Run untrusted code and risky agent actions in an isolated environment. Minimize internet access, credentials, and permissions to what the task requires.
- Establish baselines. Use defined tasks and scoring rules to understand performance before focusing on observed weaknesses.
- Test selected weaknesses. Keep each task tied to the stated threat model and record deviations from the plan.
- Escalate carefully. Use qualified experts for red-teaming where justified, within the same authorized boundaries and stop conditions.
Do not treat a model’s final message as the entire result in an agentic task. Preserve the trajectory: prompts, outputs, tool calls, execution results, attempts, relevant environment state, and settings. Record whether scores came from automated checks, model-assisted judging, or human review, along with the criteria used.
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Interpret benchmarks in their test context
Scores depend on which tasks were selected, the number of attempts, available tools, baselines, time limits, and the surrounding agent setup. A result applies to the tested tasks and conditions; it does not establish performance across all cybersecurity work or for other open-weight models.
A joint US and UK AI Safety Institute report from December 2024 illustrates why the context matters. US AISI tested OpenAI o1 on Cybench, a set of 40 challenges drawn from public capture-the-flag competitions, and estimated a Pass@10 success rate of 45%, compared with 35% for its best evaluated reference model. UK AISI tested 47 challenges—15 public and 32 privately developed—and reported Pass@10 results of 79% for o1 on technical-non-expert tasks versus 90% for its best reference model, and 46% on cybersecurity-apprentice tasks versus 46% for its best reference model. These are results for o1 in those specific 2024 suites, not findings about open-weight models generally.
The report describes its tasks as a relatively narrow slice of possible cyber activity and identifies wider task coverage, more realistic challenges, human baselines, expert-operator interaction, and clearer comparisons of task time and attempts as areas needing further work. When comparing an evaluation option, check whether its coverage, task realism, reproducibility, tools, attempt budget, time limits, scoring reliability, human baselines, monitoring, and isolation match the question you need answered.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Assess safeguards as testable claims
Translate policy statements into concrete, threat-specific requirements. Document the system, access, and maintenance safeguards that are supposed to meet them, then collect evidence with appropriately scoped tests. Depending on the claim, that evidence might come from red-teaming, static tests on existing datasets, or robustness evaluations; a third party may be useful to gather or assess it.
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Safeguards need reassessment after deployment, after material model changes, and as new attacks emerge. A test that a model refuses a few known prompts is not evidence that it will resist the full range of relevant attacks or remain effective after updates.
Report exactly what the result covers
A useful report lets operators judge the result without mistaking it for a broader assurance. Include the model version and configuration; task sources and scope; number of tasks and attempts; tools and environment; scoring method; baselines; results; failures; and limitations. Distinguish controlled benchmark performance from evidence about real-world attacker impact, and communicate known failure modes to downstream users.
Repeat evaluations after major model updates as if assessing a new version. Version-specific evidence matters because a result for one artifact and configuration cannot automatically be carried over to a changed model or deployment.
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