| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - text-classification |
| - text-generation |
| tags: |
| - cybersecurity |
| - cve |
| - cwe |
| - vulnerability |
| - security |
| pretty_name: CVE-to-CWE Consensus |
| size_categories: |
| - 100K<n<1M |
| dataset_info: |
| features: |
| - name: messages |
| list: |
| - name: role |
| dtype: string |
| - name: content |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 28397024 |
| num_examples: 50074 |
| - name: validation |
| num_bytes: 5966749 |
| num_examples: 11052 |
| - name: test |
| num_bytes: 5748623 |
| num_examples: 10514 |
| download_size: 39961558 |
| dataset_size: 40112396 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| - split: test |
| path: data/test-* |
| --- |
| |
| # 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](https://unsloth.ai)). 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. |
| - **71,640 examples** — train 50,074 / validation 11,052 / test 10,514. *(v3 — see "What's new in v3")* |
| - **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. |
|
|
| ## What's new in v3 |
|
|
| This `v3` revision is re-weighted to train models on the cases they actually fail |
| (inferring the CWE when the description does not name the weakness), and to evaluate |
| them honestly. Changes vs. the previous revision: |
|
|
| - **Low-information filter:** bare impact-title descriptions ("`<Product> <Impact> |
| Vulnerability`") that name no mechanism are dropped (~1.5% of rows; the CWE is |
| not derivable from the text). |
| - **Hard-weighted training cap, train split only:** when a common CWE exceeds the |
| per-class cap (lowered to **1,500**), the **train** split keeps inference-"hard" |
| rows (weakness not named) over easy lexical-match rows. **Validation/test are |
| capped without this weighting**, so they keep real-world prevalence — train is |
| ~20% lexical-easy, val/test ~44%. Evaluate on the stratified easy/hard numbers. |
| - **69 reviewed label corrections:** gold labels that two independent models flagged |
| and a human confirmed against the MITRE CWE definition (e.g. a CSRF labeled XSS, |
| an SSRF labeled "improper encoding") override the computed consensus. |
|
|
| Models trained on the previous revision were trained on the un-reweighted data. |
|
|
| ## Example row |
|
|
| ```json |
| {"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](https://nvd.nist.gov/developers/vulnerabilities) — analyst-assigned CWEs (`weaknesses`). |
| - [CVEProject/cvelistV5](https://github.com/CVEProject/cvelistV5) — CNA-supplied CWEs (`problemTypes`) and the English description. |
| - [MITRE CWE](https://cwe.mitre.org) 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. |
|
|
| ## Provenance (reproducibility) |
|
|
| | Source | Version | |
| |---|---| |
| | cvelistV5 | commit `7f860dcfb8260ccff2f23b4b5f685303fd314f17` | |
| | NVD | pulled 2026-05-29 | |
| | MITRE CWE | v4.20 | |
|
|
| ## Statistics |
|
|
| - **Keep rate:** 115,636 of 354,163 scanned CVEs reach consensus (~33%); after the low-information filter, the rarest-CWE floor, and hard-weighted capping, **71,640** rows are exported. Most drops are "no consensus" (sources disagreed, or only one assigned a CWE). |
| - **Easy/hard composition (v3):** the weakness is lexically named in ~**20%** of train rows (hard-weighted) vs ~**44%** of validation/test (natural prevalence). |
| - **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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
