Datasets:
metadata
task_categories:
- text-classification
language:
- en
license: cc-by-4.0
library_name: datasets
tags:
- vulnerability
- cybersecurity
- security
- cve
- cvss
vulnerability-scores
This dataset comprises 745,736 real-world vulnerabilities used to train and evaluate VLAI, a transformer-based model designed to predict software vulnerability severity levels directly from text descriptions, enabling faster and more consistent triage.
The dataset is presented in the paper VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification.
Sources
| Source | Label | Entries | Share |
|---|---|---|---|
cvelistv5 |
CVE Program (enriched with vulnrichment and Fraunhofer FKIE) | 351,655 | 47.2% |
github |
GitHub Security Advisories | 350,441 | 47.0% |
csaf_redhat |
CSAF Red Hat | 26,730 | 3.6% |
pysec |
PySec advisories | 6,991 | 0.9% |
csaf_cisa |
CSAF CISA | 5,976 | 0.8% |
csaf_cisco |
CSAF Cisco | 3,943 | 0.5% |
Extracted from the database of Vulnerability-Lookup with the VulnTrain project. Dumps of the data are available here.
Splits
| Split | Examples |
|---|---|
| train | 671,162 |
| test | 74,574 |
Fields
| Field | Type | Description |
|---|---|---|
id |
string | Vulnerability identifier (e.g., CVE-2024-1234, GHSA-xxxx, PYSEC-2024-xxx) |
title |
string | Vulnerability title |
description |
string | Vulnerability description in English |
cpes |
list[string] | Common Platform Enumeration identifiers |
cvss_v4_0 |
float | CVSS v4.0 score |
cvss_v3_1 |
float | CVSS v3.1 score |
cvss_v3_0 |
float | CVSS v3.0 score |
cvss_v2_0 |
float | CVSS v2.0 score |
patch_commit_url |
string | URL to the patch commit on GitHub, if available |
source |
string | Data source identifier |
Usage
import json
from datasets import load_dataset
dataset = load_dataset("CIRCL/vulnerability-scores")
vulnerabilities = ["CVE-2012-2339", "RHSA-2023:5964", "GHSA-7chm-34j8-4f22", "PYSEC-2024-225"]
filtered_entries = dataset.filter(lambda elem: elem["id"] in vulnerabilities)
for entry in filtered_entries["train"]:
print(json.dumps(entry, indent=4))
Related models
- CIRCL/vulnerability-severity-classification-roberta-base — RoBERTa severity classifier
- CIRCL/vulnerability-severity-classification-distilbert-base-uncased — DistilBERT severity classifier
References
- Vulnerability-Lookup — the vulnerability data source
- VulnTrain — training pipeline
- ML-Gateway — inference API
- VLAI paper — Bonhomme, C., Dulaunoy, A. (2025)