1,625,017 chat conversations covering all 361,190 usable CVEs (1999–2026), built for fine-tuning cybersecurity assistants. Every known CVE in the official CVE List with severity enrichment from NVD (via the fkie-cad community feeds), rendered as English user/assistant conversations with varied phrasings, honest handling of missing data, and a per-CVE 99/1 train/validation split with zero leakage.

The schema matches oi-uae/cyber-security exactly (messages + metadata rows), so it can be concatenated with that dataset directly.

Dataset summary

Metric	Value
Total conversations	1,625,017
Train / validation	1,608,514 / 16,503 (1.02%, split by CVE — no CVE appears in both)
Unique CVEs covered	361,190 (every usable CVE, REJECTED/reserved placeholders excluded)
Severity-enriched from NVD	168,961 records
Estimated tokens	~206M (`est_tokens` = total characters ÷ 4)
Languages	English only
System prompts	29.96% of conversations
Determinism	Fully reproducible: seeded per CVE ID (MD5-based)

Data analysis

Average conversation size by task type (est. tokens): multi-turn 343 · JSON extraction 283 · full lookup 215 · mitigation 91 · references 83 · affected software 59 · severity 53 · CWE classification 45.

Task types

Each CVE yields 1–6 conversations (typically 4–5), chosen deterministically from what the record supports:

`task_type`	Conversations	What the model learns
`cve_qa`	361,190	Full record lookup — "What is CVE-2021-44228?"
`cve_severity_qa`	358,724	CVSS score, severity label, vector
`cve_affected_qa`	216,435	Affected products and version ranges
`cve_mitigation_qa`	216,261	Documented fixes, workarounds, vendor advisories
`cve_references_qa`	216,566	Reference/advisory URLs
`cve_cwe_qa`	75,584	CWE weakness classification
`cve_extraction`	108,006	Record → clean structured JSON
`cve_multiturn`	72,251	2–4 round follow-up conversations

Schema

Each row is one conversation:

{
  "messages": [
    {"role": "system", "content": "You are a cybersecurity assistant..."},
    {"role": "user", "content": "How severe is CVE-2021-44228?"},
    {"role": "assistant", "content": "CVE-2021-44228 is rated CRITICAL severity..."}
  ],
  "metadata": {
    "source": "cvelistV5+nvd",
    "domain": "vulnerability-intelligence",
    "task_type": "cve_severity_qa",
    "n_turns": 2,
    "est_tokens": 321,
    "has_system": true,
    "cve_id": "CVE-2021-44228",
    "severity": "CRITICAL",
    "published": "2021-12-10"
  }
}

messages: optional system prompt first (~30% of rows), then strictly alternating user/assistant turns ending with assistant.
n_turns: count of user+assistant messages (system excluded).
est_tokens: rough token estimate (total content characters ÷ 4).

Methodology

Sources: CVE records from the official CVEProject/cvelistV5 repository (100% coverage of published CVEs), enriched with CVSS severity from NVD data redistributed by fkie-cad/nvd-json-data-feeds (CC0). Where the CVE List record already carries metrics (CNA or ADP containers), the record's own values win; NVD fills gaps.
Grounding: every assistant answer is template-generated strictly from the published record — no invented facts. Missing data is stated explicitly (e.g. "No CVSS score has been assigned to this CVE yet"). Mitigation answers only cite documented fixed versions, guidance sentences from the record description, or vendor advisories in the references.
Variety: 8–10 user phrasings and 1–3 assistant response structures per task type, selected by a per-CVE seeded RNG, so the model does not overfit a single wording.
Determinism: the same raw data always produces the identical dataset (MD5-seeded per CVE ID, stable across processes).
Filtering: English-only descriptions; REJECTED and placeholder records excluded.

Limitations

497 rows carry severity: "NONE" — the honest CVSS label for a 0.0 base score, outside the CRITICAL/HIGH/MEDIUM/LOW/UNRATED set.
Some affected-product strings render the record's literal n/a placeholder (e.g. n/a/n/a); this mirrors the source record faithfully.
est_tokens is a character-based heuristic (÷4), not a tokenizer count.
Answers reflect the CVE List at build time (September 2026); records are amended upstream over time.
No exploit-availability (KEV/EPSS) or patch-status signals — only what the published CVE record contains.

Uses

Fine-tuning a CVE-aware cybersecurity assistant (lookup, triage, vulnerability-intelligence Q&A).
Structured extraction training (natural language → record JSON).
Evaluation on held-out CVEs (the validation split contains full conversations for ~3,600 unseen CVEs).

Sources & licensing

CVE List v5 — © MITRE/the CVE Program, used under the CVE Terms of Use. All CVE data is attributable to the CVE Program and its CNAs.
NVD enrichment — NIST NVD data (US government public domain) as redistributed by fkie-cad/nvd-json-data-feeds under CC0.

Merging with `oi-uae/cyber-security`

The schema is identical, so rows concatenate directly:

from datasets import load_dataset, concatenate_datasets
mine = load_dataset("oi-uae/CVEs")
other = load_dataset("oi-uae/cyber-security", "full")
merged = concatenate_datasets([other["train"], mine["train"]])

Maintenance

The dataset is fully reproducible. Rebuild with:

python3 scripts/download.py raw
python3 scripts/build_dataset.py --raw raw --out data --cleanup

Only per-year NVD feed files are consumed; the build aborts before writing if zero records parse, so a failed extraction can never overwrite a good dataset. Regenerate the charts with python3 scripts/make_charts.py.

Acknowledgements

Thanks to the CVE Program, its CNAs, and NIST NVD for publishing the underlying vulnerability data, and to oi-uae for the parent cyber-security dataset whose schema this follows.