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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

References