vulnerability-scores

This dataset comprises 715,677 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)	338,811	47.3%
github	GitHub Security Advisories	338,081	47.2%
csaf_redhat	CSAF Red Hat	25,284	3.5%
csaf_cisa	CSAF CISA	5,877	0.8%
csaf_cisco	CSAF Cisco	3,934	0.5%
pysec	PySec advisories	3,690	0.5%

Extracted from the database of Vulnerability-Lookup with the VulnTrain project. Dumps of the data are available here.

Splits

Split	Examples
train	644,109
test	71,568

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)