README.md

---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: title
    dtype: string
  - name: description
    dtype: string
  - name: patches
    list:
    - name: commit_message
      dtype: string
    - name: patch_text_b64
      dtype: string
    - name: url
      dtype: string
  - name: cwe
    dtype: string
  splits:
  - name: train
    num_bytes: 4405036770
    num_examples: 35334
  - name: test
    num_bytes: 489448530
    num_examples: 3926
  download_size: 2239993030
  dataset_size: 4894485300
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

### Description

This dataset, CIRCL/vulnerability-cwe-patch, provides structured real-world vulnerabilities enriched with CWE identifiers and actual patches from platforms like GitHub and GitLab. It was built to support the development of tools for vulnerability classification, triage, and automated repair. Each entry includes metadata such as CVE/GHSA ID, a description, CWE categorization, and links to verified patch commits with associated diff content and commit messages.

The dataset is automatically extracted using a pipeline that fetches vulnerability records from several sources, filters out entries without patches, and verifies patch links for accessibility. Extracted patches are fetched, encoded in base64, and stored alongside commit messages for training and evaluation of ML models.
Source Data

The dataset comprises 39,260 vulnerabilities and **49,001 associated patches**. For training, we consider only those patches corresponding to vulnerabilities annotated with at least one CWE.


### How to use with datasets

```python
>>> import json
>>> from datasets import load_dataset

>>> dataset = load_dataset("CIRCL/vulnerability-cwe-patch")

>>> vulnerabilities = ["CVE-2025-60249", "CVE-2025-32413"]

>>> filtered_entries = dataset.filter(lambda elem: elem["id"] in vulnerabilities)

>>> for entry in filtered_entries["train"]:
...     print(entry["cwe"])
...     for patch in entry["patches"]:
...         print(f"  {patch['commit_message']}")
...
CWE-79 Improper Neutralization of Input During Web Page Generation (XSS or 'Cross-site Scripting')     
  [PATCH] fix: [security] Fixed a stored XSS vulnerability in user bios. Thanks to Dawid Czarnecki for reporting the issue.
CWE-79 Improper Neutralization of Input During Web Page Generation (XSS or 'Cross-site Scripting')
  [PATCH] fix: [security] sanitize user input in comments, bundles, and sightings - Escaped untrusted data in templates and tables to prevent XSS - Replaced unsafe innerHTML assignments with createElement/textContent - Encoded dynamic URLs using encodeURIComponent - Improved validation in Comment, Bundle, and Sighting models Credit: @Wachizungu
```


### Schema

Each example contains:

    - id: Vulnerability identifier (e.g., CVE-2023-XXXX, GHSA-XXXX)

    - title: Human-readable title of the vulnerability

    - description: Detailed vulnerability description

    - patches: List of patch records, each with:

        url: Verified patch URL (GitHub/GitLab)

        patch_text_b64: Base64-encoded unified diff

        commit_message: Associated commit message

    - cwe: List of CWE identifiers and names


The vulnerabilities can be sourced from:

    - NVD CVE List — enriched with commit references

    - GitHub Security Advisories (GHSA)

    - GitLab advisories

    - CSAF feeds from vendors including Red Hat, Cisco, and CISA


### Use Cases

The dataset supports a range of security-focused machine learning tasks:

    * Vulnerability classification
    
    * CWE prediction from descriptions
    
    * Patch generation from natural language
    
    * Commit message understanding

### Associated Code

The dataset is generated with the extraction pipeline from vulnerability-lookup/ML-Gateway, which includes logic for fetching, filtering, validating, and encoding patch data.