ZéroJour is a project testing whether structured security-advisory data helps an AI agent answer compound, version-sensitive questions. Its author reports that structured retrieval answered 9 of 11 evaluation questions, compared with 1 of 11 for flat keyword search and 0 of 11 for a model without data tools. Those are project-reported results, not an independently reproduced benchmark—and the project’s own examples show that structured data does not prevent reasoning mistakes.
What ZéroJour is designed to answer
ZéroJour is not presented as a general vulnerability scanner. It tests a narrower question: can an agent retrieve and combine advisory details reliably when a question depends on package versions, vulnerability attributes, or remediation information?
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For example, a user might ask which advisories affect exactly axios 1.2.0, or which advisories are network-reachable, score at least 8.0, require neither privileges nor user interaction, and what fixes are available. Answering such questions requires more than finding documents that share a keyword: the system must apply version-range logic, filter several fields, and connect results to fixes.
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Author Sam LABBE describes three parts: an advisory corpus, an agent that uses Sanity Context and a local deterministic tool, and an evaluation harness comparing structured retrieval with keyword search and a no-tools control. The author says the corpus contains 82 advisories covering 16 npm and pip packages, 50 CWEs, and 16 remediation playbooks. The reported sources are the GitHub Advisory Database and CISA KEV catalog; withdrawn and malware advisories are excluded, and the corpus can be rebuilt from live sources. These figures and design details are the author’s account of the project.
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The described advisory schema includes affected version ranges, CVSS components parsed from the official vector, fix status, exploit maturity, and CWE references. The author says exploit maturity is included only when sourced from CISA KEV, rather than inferred. Remediation guidance is stored separately in playbook documents. The agent connects to both a GROQ dataset endpoint and a Knowledge Base endpoint.
What the reported evaluation found
The author says the evaluation used 11 questions and compared three arms using the same question set. Ground truth was generated separately from seed data using plain predicates and semver rather than through Sanity. The reported scores were unchanged across two independent full runs:
| Evaluation arm | What it could access | Reported correct |
|---|---|---|
| Structured Sanity Context retrieval | Structured GROQ and Knowledge Base retrieval | 9 of 11 questions |
| Flat keyword search | Keyword search over the same documents | 1 of 11 questions |
| No-tools model control | No data access | 0 of 11 questions |
These small, author-reported results suggest a practical advantage for structured retrieval on the tested questions, which involved version-range intersections, CVSS-component filters, fix status, package-level aggregation, and recency ordering. They do not establish performance across other advisory collections, question sets, or agents. The author’s summary is: “A keyword search cannot do that. We didn’t argue it, we measured it.”
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Where the agent still made mistakes
Structured retrieval helps find and filter data; it does not guarantee that a model will aggregate or order the results correctly. The author reports two repeatable misses:
- Counting qualifying advisories: Asked which package had the most advisories scoring at least 7.0, the model chose paramiko. The author says the dataset’s answer was pillow, with four qualifying advisories compared with two for paramiko.
- Finding the newest advisory: The model chose a 2018 advisory because it followed the default storage order instead of sorting by publication date from newest to oldest.
The author says prompt changes did not correct these errors. That distinction matters: a system can retrieve relevant records yet still fail at the final reasoning step.
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Why version checks use a deterministic tool
GROQ compares version strings lexicographically, which is not the same as semantic version ordering. For example, text sorting can place “1.10.0” before “1.9.0.” The described agent therefore delegates version-range checks to a local deterministic semver tool rather than relying on a GROQ string comparison. For version-sensitive advisory questions, that separation helps avoid treating version labels as ordinary text.
Replaying the evaluation and refreshing data
The project article gives these commands for running the local evaluation and refreshing the advisory corpus. They are project instructions reported by the author; they have not been independently run or verified here.
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npm install && npm run evalruns the evaluation replay.npm run fetch:advisoriesrefreshes the corpus.
What the audit journal does—and does not establish
The project says each query, MCP call, answer, and evaluation verdict is recorded in a local NoireBox journal using a SHA-256 hash chain, Ed25519 signatures, and RFC 3161 timestamps. This is the project’s stated audit mechanism. Those implementation details alone do not amount to an independent security certification or validate the journal’s guarantees; no independent assessment of the journal is established by the available project account.
How to interpret the result
ZéroJour’s reported comparison is evidence that structured advisory fields can make compound retrieval tasks easier than flat keyword search in this particular 11-question test. Its limitations are equally instructive: version comparisons need semantics-aware handling, and correct retrieval does not ensure correct aggregation or recency ordering. The scores are author-reported and have not been independently reproduced, so they should be read as a project demonstration, not a general benchmark of AI security tools.
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