Datasets:
metadata
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: description
dtype: string
- name: cpes
sequence: string
- name: cvss_v4_0
dtype: float64
- name: cvss_v3_1
dtype: float64
- name: cvss_v3_0
dtype: float64
- name: cvss_v2_0
dtype: float64
splits:
- name: train
num_bytes: 363023583.0092845
num_examples: 559803
- name: test
num_bytes: 40336385.990715496
num_examples: 62201
download_size: 158862200
dataset_size: 403359969
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- text-classification
license: cc-by-4.0
library_name: datasets
tags:
- vulnerability
- cybersecurity
- security
- cve
- cvss
This dataset, CIRCL/vulnerability-scores, comprises over 600,000 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.
Project page: https://vulnerability.circl.lu Associated code: https://github.com/vulnerability-lookup/ML-Gateway
Sources of the data
- CVE Program (enriched with data from vulnrichment and Fraunhofer FKIE)
- GitHub Security Advisories
- PySec advisories
- CSAF Red Hat
- CSAF Cisco
- CSAF CISA
Extracted from the database of Vulnerability-Lookup.
Dumps of the data are available here.
Query with datasets
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))