id
stringlengths 12
19
| title
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237
| description
stringlengths 35
3.87k
⌀ | patches
listlengths 1
22
| cwe
stringlengths 2
440
⌀ |
|---|---|---|---|---|
CVE-2021-23758
|
Deserialization of Untrusted Data
| "All versions of package ajaxpro.2 are vulnerable to Deserialization of Untrusted Data due to the po(...TRUNCATED)
| [{"commit_message":"[PATCH] added allowed customized types .gitignore (...TRUNCATED)
|
Deserialization of Untrusted Data
|
CVE-2022-4455
|
sproctor php-calendar index.php cross site scripting
| "A vulnerability was identified in sproctor php-calendar up to 2.0.13. This impacts an unknown funct(...TRUNCATED)
| [{"commit_message":"[PATCH] Attempt to mitigate reflective XSS attack index.php | 4 ++-- 1 file chan(...TRUNCATED)
|
Cross Site Scripting
|
GHSA-v5x8-2g8c-7279
| null | [{"commit_message":"[PATCH] Bugfix: Possible SQL injection in nat/item-add-submit.php. Fixes #2344 a(...TRUNCATED)
| null |
|
GHSA-vxwr-wpjv-qjq7
|
XWiki Platform: Privilege escalation (PR) from user registration through PDFClass
| null | [{"commit_message":"[PATCH] XWIKI-21337: Apply PDF templates with the rights of their authors (cherr(...TRUNCATED)
| null |
GHSA-hm2w-8xwc-gpv5
| null | [{"commit_message":"[PATCH] Fix invalid free in RAnal.avr libr/anal/p/anal_avr.c | 2 +- 1 file chang(...TRUNCATED)
| null |
|
GHSA-9r4g-gmfh-6gxg
| null | [{"commit_message":"[PATCH] Fix a reflected cross-site scripting vulnerability CVE-2022-1187 inc/adm(...TRUNCATED)
| null |
|
CVE-2022-24976
| "Atheme IRC Services before 7.2.12, when used in conjunction with InspIRCd, allows authentication by(...TRUNCATED)
| [{"commit_message":"[PATCH] saslserv/main: Track EID we're pending login to The existing model does (...TRUNCATED)
|
n/a
|
|
CVE-2014-125106
|
Nanopb before 0.3.1 allows size_t overflows in pb_dec_bytes and pb_dec_string.
| [{"commit_message":"[PATCH] Protect against size_t overflows in pb_dec_bytes/pb_dec_string. Possible(...TRUNCATED)
|
n/a
|
|
GHSA-76p3-8jx3-jpfq
|
Prototype pollution in webpack loader-utils
| null | [{"commit_message":"[PATCH] fix: security problem (#220) lib/parseQuery.js | 2 +- 1 file changed, 1 (...TRUNCATED)
| null |
GHSA-g44v-6qfm-f6ch
|
Answer has Guessable CAPTCHA
| null | [{"commit_message":"[PATCH] update VerifyCaptcha internal/repo/captcha/captcha.go | 8 ++++(...TRUNCATED)
| null |
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 16,123 vulnerabilities and 18,810 associated patches. For training, we consider only those patches corresponding to vulnerabilities annotated with at least one CWE.
How to use with datasets
>>> 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.
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