exploitintel/cve-cwe-consensus

CVE-to-CWE Consensus Dataset

A multi-label dataset mapping CVE vulnerability descriptions to their CWE weakness type(s), built for fine-tuning instruction-tuned LLMs (e.g. with Unsloth). Each label is a consensus assignment: a CWE is kept only when NVD and the CVE Numbering Authority (CNA) independently agree on it, after rolling both up to CWE View-1003 (the ~130-weakness "Weaknesses for Simplified Mapping of Published Vulnerabilities").

TL;DR

• Task: given a CVE description, predict the CWE ID(s) — multi-label.
• 69,386 examples (capped/balanced build) — train 55,810 / validation 6,774 / test 6,802.
• Format: conversational messages (system / user / assistant), ready for chat-template SFT.
• Trust model: labels are the intersection of two independent official sources (NVD + CNA), so single-source errors are excluded by construction.

Example row

{"messages": [
  {"role": "system", "content": "You are a vulnerability analyst. Given a CVE description, reply with only the CWE ID(s) it maps to, comma-separated."},
  {"role": "user", "content": "A reflected cross-site scripting issue in Acme Portal lets a remote attacker inject arbitrary script via the q parameter."},
  {"role": "assistant", "content": "CWE-79"}
]}

Multi-label targets are comma-separated and numerically sorted, e.g. "CWE-79, CWE-352". The model input is the description only — CVE IDs and source label fields are never included, to avoid memorization/leakage.

How it was built

1. Sources (official upstreams only):
   • NVD CVE API 2.0 — analyst-assigned CWEs (weaknesses).
   • CVEProject/cvelistV5 — CNA-supplied CWEs (problemTypes) and the English description.
   • MITRE CWE catalog (v4.20) — View-1003 membership + the ChildOf hierarchy.
2. Roll-up: every assigned CWE is rolled up to its nearest View-1003 mapping ancestor(s).
3. Consensus: the label is the intersection of NVD's and the CNA's rolled-up CWE sets; CVEs with no agreement are dropped.
4. Filtering: REJECTED/DISPUTED CVEs, non-English and too-short (<40 char) descriptions are dropped; pseudo-labels (NVD-CWE-noinfo/-Other) are excluded.
5. De-duplication: near-duplicate descriptions (MinHash, Jaccard ≥ 0.7) are grouped into families, and whole families stay within a single split, so near-identical text cannot leak across train/validation/test (verified: 0 shared descriptions across splits).
6. Split: dedup-family-aware, ~80/10/10.
7. Balance (cap): each CWE label is capped at 2,500 examples (single seeded shuffle) so common classes don't swamp the long tail — see Statistics.

Provenance (reproducibility)

Source	Version
cvelistV5	commit 7f860dcfb8260ccff2f23b4b5f685303fd314f17
NVD	pulled 2026-05-29
MITRE CWE	v4.20

Statistics

• Consensus keep rate: 116,793 of 354,163 scanned CVEs reach consensus (~33%); most drops are "no consensus" (sources disagreed, or only one assigned a CWE).
• Balanced/capped build: majority CWEs are then capped at 2,500 examples each, reducing the 116,793 consensus rows to the 69,386 published rows (train 55,810 / val 6,774 / test 6,802). This drops the top-10 label share from 55% to **31.5%**; all 117 labels appear in every split. The pre-cap natural distribution remains in this repo's git history (and is reproducible with max_examples_per_cwe: null).
• Recency skew: consensus concentrates on 2023+ CVEs, because that is when CNAs began reliably populating CWE — this is the main coverage bias.
• Label space: 127 distinct View-1003 CWEs appear in the consensus set; a minimum-examples floor (50) prunes the rarest, leaving ~117 effective labels. The distribution is long-tailed (CWE-79 most common).
• Cardinality: most examples carry a single label; ~14% carry two or more.

Intended use

Fine-tuning or evaluating models that classify CVE descriptions into CWE types (vulnerability triage, enrichment, prioritization aids). Held-out test is intended for measuring multi-label performance (e.g. exact-match and micro/macro F1).

Limitations & biases

• Recency-skewed (2023+) — weaker coverage of older CVE styles.
• Consensus is high-precision, not exhaustive — CVEs where the two sources disagree (often genuinely ambiguous) are excluded, so this set is "cleaner/easier" than the full CVE population.
• Roll-up to View-1003 discards sub-View-1003 specificity (variant-level CWEs map to their base).
• English only.
• Not a substitute for analyst review on novel or complex vulnerabilities.

License & attribution

Released under CC BY 4.0. The dataset derives from public sources; please also honor their terms:

• NVD (NIST) — U.S. Government work, public domain.
• CVE Program / cvelistV5 — © the CVE Program; CVE Records are free to use with attribution.
• MITRE CWE — © The MITRE Corporation; free to use with attribution.

CWE™ and CVE® are trademarks of The MITRE Corporation.

Citation

@misc{cve_cwe_consensus_2026,
  title        = {CVE-to-CWE Consensus Dataset},
  author       = {exploitintel},
  year         = {2026},
  howpublished = {Hugging Face Hub},
  url          = {https://huggingface.co/datasets/exploitintel/cve-cwe-consensus}
}