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A 30-record test can show how a specific model handled a specific set of security advisory records and prompts. It cannot establish a general accuracy rate unless the model, records, prompts, scoring rules, and results are disclosed. Because those particulars and the test outputs are not identified here, no winner or accuracy percentage can be reported. The useful outcome is a reproducible method for running and evaluating the test without turning a small sample into a broad claim.
What a 30-record test can—and cannot—tell you
The test measures the answers produced by one defined model configuration against one defined set of 30 records. Its result depends on the records selected, the information shown to the model, the prompt, the tools available, and the scoring method. A different sample or setup can produce a different result.
Thirty records are a small sample. Unless the selection method supports broader inference, describe the result as performance on those records—not as the model’s accuracy on cybersecurity questions generally. Explain whether the sample was ordinary, adversarially selected, or chosen to probe particular failure modes. Google’s evaluation guidance likewise distinguishes adversarial evaluation from standard evaluation and recommends designing sets for the system and risks being assessed.
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Choose and preserve the source records
NIST’s National Vulnerability Database (NVD) provides vulnerability APIs and JSON 2.0 data feeds. NVD describes its Vulnerability APIs as the preferred way to stay current; the APIs support searching records and retrieving changes. Its data feeds support an initial data import followed by modified-feed synchronization.
For a repeatable test, publish the inclusion rule and preserve the exact material used. A CVE ID alone is insufficient to recreate an earlier run because the record may change. For each record, retain:
- The CVE identifier and the rule that determined its inclusion.
- The retrieval timestamp and the exact payload or a stable archived copy.
- The extraction method and the fields supplied to the model.
- The source text used as the answer key, including whether it was the current CNA description, NVD analysis, or another specified field.
NVD records can include descriptions, references, CVSS scores, and product applicability statements when NVD analysis is complete; the fields available vary by record. The API documentation describes these fields and record dates. State which fields the model saw and which fields graders used. NVD’s detail-page guidance explains that descriptions are maintained by the responsible CVE Numbering Authority (CNA), not controlled by NVD. If the CNA description and NVD analysis differ in a way that affects grading, disclose which one governs and how the discrepancy was handled.
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Fix the model setup before running it
A product label such as “free model” does not uniquely identify a system. Record enough setup information for readers to understand the run and, where the interface permits, repeat it:
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- Provider, model name, exact version or the access date if the version is not exposed.
- Interface or route used, date of access, and any relevant free-tier limits.
- System and user prompts, reproduced exactly.
- Whether web browsing, retrieval, code execution, or other tools were enabled.
- Temperature and other generation settings, if available.
- Context or input limits that affected which record fields could be supplied.
If the provider changes a model behind a stable product name and does not expose a version, say so. In that situation, the exact historical run may not be reproducible even when the prompts and records are preserved. Do not imply that a later run used the identical model configuration unless that can be established.
Score answers at the claim level
Decide the rubric before inspecting model responses. Claim-level scoring is more informative than requiring exact wording: a response can express a record’s facts accurately in different words, while a fluent answer can also introduce unsupported details. Break each answer into factual claims and compare each claim with the chosen source record.
Keep these outcomes distinct in the report:
- Correct claims: supported by the specified source text.
- Material omissions: required facts from the prompt or rubric that the answer left out.
- Unsupported additions: claims not supported by the source record, including plausible-sounding details.
- Abstentions or refusals: answers that decline to make a claim; do not silently count these as correct answers.
- Records with at least one material error: a record-level measure separate from the share of individual claims that are wrong.
OpenAI’s published factuality evaluation reports both the share of claims with factual errors and the share of responses containing at least one error. That is a useful reporting precedent, not evidence about this unnamed model or sample. The same evaluation cautions that deliberately difficult prompts are research signals, not measures of average error prevalence in production.
Also publish the per-record scores, aggregate calculations, annotator procedure, disagreements, and how disagreements were resolved. If you include exact-match grading, report it separately from semantic correctness so readers can distinguish wording differences from factual errors.
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Keep severity separate from factual accuracy
CVSS severity is not a score for whether an AI answer is true. NVD states, “CVSS is not a measure of risk.” A model can correctly repeat a CVSS score while misdescribing the vulnerability, or accurately summarize a record that has no CVSS score in the fields provided. Treat severity as record content to verify—not as a proxy for answer accuracy or real-world risk.
Report results without overstating them
A useful report makes the test auditable and its scope clear. Include the model setup, prompts, tools, record IDs and preserved snapshots, selection rule, rubric, annotator process, per-record results, and aggregate scores. Explain limitations such as unavailable model versioning, context constraints, or differences between the selected source description and later record text.
If comparing models, use the same records and prompts, disclose unequal access or context limits, and report separate measures for factual correctness, coverage of material details, unsupported claims, attribution, abstention, and rerun stability. Do not collapse these different behaviors into a single “accuracy” figure without defining exactly what it counts.
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