datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
vulnerability-cwe-patch
Description
This dataset, CIRCL/vulnerability-cwe-patch, provides structured, real-world vulnerabilities enriched with CWE identifiers and corresponding patches from platforms like GitHub and GitLab. It is designed to support the development of tools for vulnerability classification, triage, and automated remediation. 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… See the full description on the dataset page: https://huggingface.co/datasets/CIRCL/vulnerability-cwe-patch.code-security-vulnerability-dataset
Code Security Vulnerability Dataset
A curated multi-language dataset of 175,419 code samples labeled with 31 vulnerability classes (30 CWEs + safe) for training multi-label code vulnerability detection models. Labels are mapped to OWASP Top 10 2021 categories.
Dataset Details
Property
Value
Total Samples
175,419
Train / Val / Test
140,335 / 17,542 / 17,542
Languages
C, C++, Python, JavaScript, Java, PHP, Go
Labels
31 (multi-label)
Format
Parquet with… See the full description on the dataset page: https://huggingface.co/datasets/ayshajavd/code-security-vulnerability-dataset.vulnerability-scores
vulnerability-scores
This dataset comprises 798,886 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… See the full description on the dataset page: https://huggingface.co/datasets/CIRCL/vulnerability-scores.vul-mine-vulnerability-dataset
VulMine vulnerability dataset
VulMine is a strict, naturally imbalanced function/method-level vulnerability
dataset mined from public OSV advisories and immutable public Git revisions.
The default configuration covers Java, Python, and JavaScript. The c-cpp
configuration is a C/C++ language control built with the same VulMine label and
cleaning principles for comparisons with Big-Vul.
Release
The default configuration contains vulmine-clean-v1.2.
Split… See the full description on the dataset page: https://huggingface.co/datasets/ryal-xyz/vul-mine-vulnerability-dataset.vulnerability
Dataset Card for Dataset Name
This dataset has been generated with:
https://github.com/vulnerability-lookup/VulnTrain
Based on data from the Vulnerability-Lookup instance operated by CIRCL:
https://vulnerability.circl.lu/
The dataset is derived from CVE data provided by NIST and enriched with information from the CVE Program, FKIE, and Vulnrichment.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]:… See the full description on the dataset page: https://huggingface.co/datasets/CIRCL/vulnerability.unified-vulnerability-intelligence-dataset
Unified Vulnerability Intelligence Dataset (UVID) v3.0 — Cyber Security Knowledge Graph
UVID is a structured cyber security knowledge graph that unifies multiple
vulnerability classification frameworks into a single knowledge base. Each of the
250 records describes one application/software security vulnerability and links
it — where authoritative data exists — across CWE, CAPEC, MITRE ATT&CK, CVSS,
14 OWASP projects, secure-fix intelligence, detection surfaces, programming… See the full description on the dataset page: https://huggingface.co/datasets/ismailtasdelen/unified-vulnerability-intelligence-dataset.SecCoderX_Reasoning_Vulnerability_Detection_SFT_Cold_Start_Dataset
Citation
If you find our work helpful, feel free to give us a cite.
@misc{wu2026securecodegenerationonline,
title={Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model},
author={Tianyi Wu and Mingzhe Du and Yue Liu and Chengran Yang and Terry Yue Zhuo and Jiaheng Zhang and See-Kiong Ng},
year={2026},
eprint={2602.07422},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2602.07422}… See the full description on the dataset page: https://huggingface.co/datasets/SecCoderX/SecCoderX_Reasoning_Vulnerability_Detection_SFT_Cold_Start_Dataset.vulnerability-attack-techniques
vulnerability-attack-techniques
This dataset maps 1,207 CVEs to MITRE ATT&CK (Enterprise) techniques, joining
hand-curated mappings from the MITRE Center for Threat-Informed Defense (CTID)
with vulnerability descriptions from
CIRCL/vulnerability-scores.
It is intended for training and evaluating models that suggest candidate ATT&CK
techniques from a vulnerability description: CVSS tells you how bad a
vulnerability is, CWE what kind of flaw it is — ATT&CK tells defenders what… See the full description on the dataset page: https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques.Code-Vulnerability-FineTune
🔐 Code Vulnerability FineTome — CWE-Enriched Conversation Dataset
📌 Overview
This dataset converts raw security-labeled C/C++ code samples into instruction-following conversation pairs suitable for fine-tuning large language models (LLMs) on software vulnerability detection and analysis.
It is built by preprocessing and transforming the ChamaraVishwajithRajapaksha/Code_Vulnerability_Dataset (330k rows, sourced from DiverseVul + MITRE CWE enrichment) into… See the full description on the dataset page: https://huggingface.co/datasets/ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune.Vulnerability-CNVD
Vulnerability-CNVD
Vulnerability descriptions and severity labels from the China National Vulnerability Database (CNVD), extracted via Vulnerability-Lookup.
Dataset structure
Field
Type
Description
id
string
CNVD identifier (e.g., CNVD-2025-03529)
title
string
Vulnerability title in Chinese
description
string
Vulnerability description in Chinese
severity
string
Severity level: 高 (High), 中 (Medium), or 低 (Low)
cve_id
string
Corresponding CVE… See the full description on the dataset page: https://huggingface.co/datasets/CIRCL/Vulnerability-CNVD.vulnerability-intelligence-diagrammatic-reasoning
Vulnerability Intelligence with Diagrammatic Reasoning
[!Important]
This dataset was created as a proof-of-concept for the Reasoning Datasets Competition (May 2025). If you have any feedback or suggestions, please feel free to open a discussion! Access the Github repository here.
A. Overview
This dataset focuses on security vulnerability analysis through a multi-dimensional approach that combines four types of reasoning to generate valuable insights for… See the full description on the dataset page: https://huggingface.co/datasets/davidquicast/vulnerability-intelligence-diagrammatic-reasoning.vulnerability-severity-classificationThis dataset combines vulnerable functions (scraped from 5 auditting companies: Codehawks, ConsenSys, Cyfrin, Sherlock, Trust Security) and auddited functions with no vulnerabilities (scraped from Etherscan)
The purpose of the dataset is to enable training of classification models to discriminate between the 4 classes: none, low, medium and high.
Field
Description
1. function
Raw solidity code
2. severity
Severity of vulnerability ('none', low, medium, high)
Data… See the full description on the dataset page: https://huggingface.co/datasets/msc-smart-contract-auditing/vulnerability-severity-classification.SARD-Vulnerability-Datasetvulnerability-attack-techniques-llm-scaling
vulnerability-attack-techniques-llm-scaling
⚠️ The labels in this dataset are machine-generated by an LLM, not
analyst-curated — and the paper that produced them found they do not
improve a classifier trained on the expert gold set. It is published for
reproducibility and for research on LLM-assisted labeling. For training,
use the curated gold set
CIRCL/vulnerability-attack-techniques.
This dataset contains 984 CVEs labeled with MITRE ATT&CK (Enterprise)
techniques by… See the full description on the dataset page: https://huggingface.co/datasets/CIRCL/vulnerability-attack-techniques-llm-scaling.OpenVul_Rejection_Sampling_based_Vulnerability_Reasoning_Dataset_for_SFTThis dataset provides high-quality, correctness-filtered vulnerability reasoning data to support the SFT of specialized VD LLMs for future research.
vulnerability-specifications
VulInstruct: Specification-Guided Vulnerability Detection 🔒
📄 Paper |
🤗 Dataset |
💻 GitHub
Introduction
This repository contains the Specification Knowledge Base introduced in the paper VulInstruct: Teaching LLMs Root-Cause Reasoning for Vulnerability Detection via Security Specifications.
VulInstruct is a specification-guided approach that systematically extracts security specifications—expectations about how code should behave to remain safe—from… See the full description on the dataset page: https://huggingface.co/datasets/hzhu721/vulnerability-specifications.vulnerability-scores-cvss-v3
Vulnerability scores (CVSS v3 combined)
A labeled slice of CIRCL/vulnerability-scores for training and evaluating models that predict CVSS v3 severity from a vulnerability description.
Every row has a combined v3 score and a severity band. Rows with no v3.1 or v3.0 score were dropped.
What changed from the original
The CIRCL dataset stores four separate CVSS columns (cvss_v4_0, cvss_v3_1, cvss_v3_0, cvss_v2_0). Those versions are not on the same scale, so this… See the full description on the dataset page: https://huggingface.co/datasets/AgileRLArena/vulnerability-scores-cvss-v3.OpenVul_Distilled_Vulnerability_Reasoning_CoTs_from_DeepSeek-R1-0528This dataset provides all training data's vulnerability reasoning CoTs (with 8 generations per sample) distilled from DeepSeek-R1-0528. This dataset has not been filtered for correctness and can be used to construct vulnerability reasoning and preference datasets for future research.
vulnerability-reports
SPECA Vulnerability Reports
A normalized corpus of public smart-contract audit findings, curated by
the SPECA project. Each row is
one expert-judged audit issue, deduplicated across public competitive-audit
platforms.
The dataset is split into one config per security domain (defi,
lending, oracle, …). HuggingFace auto-detects each <domain>/
folder as a config; pick the domain you want at load time.
Loading
from datasets import load_dataset
# A specific domain… See the full description on the dataset page: https://huggingface.co/datasets/NyxFoundation/vulnerability-reports.Code-Vulnerability-Balanced
Code Vulnerability Balanced — CWE-Enriched Conversation Dataset
📌 Overview
This dataset is a balanced and shuffled version of
ChamaraVishwajithRajapaksha/Code-Vulnerability-FineTune,
which itself was derived from the original
ChamaraVishwajithRajapaksha/Code_Vulnerability_Dataset
(330k rows, sourced from DiverseVul + MITRE CWE enrichment).
The original fine-tuning dataset was imbalanced — the number of Vulnerable and Safe
samples were not equal — and the samples were not… See the full description on the dataset page: https://huggingface.co/datasets/ChamaraVishwajithRajapaksha/Code-Vulnerability-Balanced.vulnerability-triage
VULNERABILITY_TRIAGE
A preference dataset for VULNERABILITY_TRIAGE, harvested from real, human-labelled sources and curated by an automated harvesting harness with an LLM quality gate.
Format
Standard preference / DPO schema — each row:
column
meaning
prompt
the request (originally prompt)
chosen
the human-preferred response
rejected
a worse response to the same prompt
source
the dataset/URL the row was harvested from
Splits… See the full description on the dataset page: https://huggingface.co/datasets/316usman/vulnerability-triage.Code_Vulnerability_Dataset
🔐 Code Vulnerability Dataset (CWE-Enriched)
📌 Overview
This dataset is built from the bstee615/diversevul dataset and enhanced with structured vulnerability intelligence from the MITRE Common Weakness Enumeration (CWE) database.
It provides a rich, machine-readable representation of software vulnerabilities, mapping raw vulnerable code samples to standardized CWE classifications.
The dataset is designed for research and development in:
Vulnerability detection models… See the full description on the dataset page: https://huggingface.co/datasets/ChamaraVishwajithRajapaksha/Code_Vulnerability_Dataset.cosyvoice-clone
LALM Emotional Vulnerability Dataset
Overview
This dataset contains synthesized malicious speech instructions across multiple emotions and intensity levels to evaluate the safety responsiveness of Large Audio-Language Models (LALMs). The dataset aims to examine how speaker emotion and intensity influence the safety and robustness of AI responses.
Dataset Composition
Total samples: 8,320
Emotion categories:
Neutral: 520 samples
Angry: 1560 samples… See the full description on the dataset page: https://huggingface.co/datasets/LALM-emotional-vulnerability/cosyvoice-clone.circl-vulnerability-scores
Mirror: CIRCL/vulnerability-scores
Pinned snapshot / mirror of CIRCL/vulnerability-scores, re-hosted for PROTISEC
research reproducibility. Redistributed under the upstream license (cc-by-4.0)
with attribution — all credit to the original author.
Original author: CIRCL
Source dataset: CIRCL/vulnerability-scores
License: cc-by-4.0
Family: cve
Mode: full
Rows cached: 731021
Changes vs upstream: cached snapshot, possibly stream-capped and/or
malformed-JSONL-repaired for… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/circl-vulnerability-scores.circl-vulnerability-attack-techniques
Mirror: CIRCL/vulnerability-attack-techniques
Pinned snapshot / mirror of CIRCL/vulnerability-attack-techniques, re-hosted for PROTISEC
research reproducibility. Redistributed under the upstream license (cc-by-4.0)
with attribution — all credit to the original author.
Original author: CIRCL
Source dataset: CIRCL/vulnerability-attack-techniques
License: cc-by-4.0
Family: cve
Mode: full
Rows cached: 1208
Changes vs upstream: cached snapshot, possibly stream-capped and/or… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/circl-vulnerability-attack-techniques.OpenVul_Vulnerability_Query_Dataset_for_RLThis dataset provides context-aware vulnerability queries partitioned chronologically by commit date into training, validation, and test sets, designed to support the RL (e.g., GRPO) of specialized VD LLMs in future research.
code_vulnerability_pythonCWE-Code_Vulnerability_Security_DPOCode_Vulnerability_Security_DPOVulnerability-FSTEC
Vulnerability-FSTEC
Vulnerability descriptions and severity labels from the FSTEC, extracted via Vulnerability-Lookup.
Source
Data source: Vulnerability-Lookup API
Extraction tool: VulnTrain
Related models
CIRCL/vulnerability-severity-classification-russian-ruRoberta-large — severity classifier trained on this dataset