# CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software

arXiv: https://arxiv.org/abs/2107.08760 · HTML: https://mlsanity.com/paper/2107.08760/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 14 of 14)

- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair](https://mlsanity.com/paper/1901.01808/) (2018)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis](https://mlsanity.com/paper/2102.07995/) (2021)
- [Applying CodeBERT for Automated Program Repair of Java Simple Bugs](https://mlsanity.com/paper/2103.11626/) (2021)
- [How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset](https://mlsanity.com/paper/1905.13334/) (2019)
- [A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software](https://mlsanity.com/paper/1902.02595/) (2019)
- [A Practical Approach to the Automatic Classification of Security-Relevant Commits](https://mlsanity.com/paper/1807.02458/) (2018)
- [End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks](https://mlsanity.com/paper/1703.02458/) (2017)
- [World of Code: Enabling a Research Workflow for Mining and Analyzing the Universe of Open Source VCS data](https://mlsanity.com/paper/2010.16196/) (2020)
- [On the Rise and Fall of Simple Stupid Bugs: a Life-Cycle Analysis of SStuBs](https://mlsanity.com/paper/2103.09604/) (2021)
- [A large-scale study on human-cloned changes for automated program repair](https://mlsanity.com/paper/2104.02386/) (2021)
- [Using Sequence-to-Sequence Learning for Repairing C Vulnerabilities](https://mlsanity.com/paper/1912.02015/) (2019)

## Built on by (most cited first, 30 of 91)

- [Large Language Models for Code: Security Hardening and Adversarial Testing](https://mlsanity.com/paper/2302.05319/) (2023)
- [Vulnerability Detection with Code Language Models: How Far Are We?](https://mlsanity.com/paper/2403.18624/) (2024)
- [DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection](https://mlsanity.com/paper/2304.00409/) (2023)
- [SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks](https://mlsanity.com/paper/2506.11791/) (2025)
- [Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG](https://mlsanity.com/paper/2406.11147/) (2024)
- [How Effective Are Neural Networks for Fixing Security Vulnerabilities](https://mlsanity.com/paper/2305.18607/) (2023)
- [LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights](https://mlsanity.com/paper/2502.07049/) (2025)
- [Finetuning Large Language Models for Vulnerability Detection](https://mlsanity.com/paper/2401.17010/) (2024)
- [Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning](https://mlsanity.com/paper/2406.03718/) (2024)
- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [Enhanced Automated Code Vulnerability Repair using Large Language Models](https://mlsanity.com/paper/2401.03741/) (2024)
- [CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection](https://mlsanity.com/paper/2503.09433/) (2025)
- [Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferences](https://mlsanity.com/paper/2506.00419/) (2025)
- [Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT](https://mlsanity.com/paper/2404.17110/) (2024)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability](https://mlsanity.com/paper/2602.03012/) (2026)
- [ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software](https://mlsanity.com/paper/2606.17283/) (2026)
- [A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2507.22659/) (2025)
- [Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview](https://mlsanity.com/paper/2503.10784/) (2025)
- [Agent That Debugs: Dynamic State-Guided Vulnerability Repair](https://mlsanity.com/paper/2504.07634/) (2025)
- [Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection](https://mlsanity.com/paper/2401.07466/) (2024)
- [Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++](https://mlsanity.com/paper/2407.17053/) (2024)
- [Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?](https://mlsanity.com/paper/2407.17803/) (2024)
- [CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval](https://mlsanity.com/paper/2506.11066/) (2025)
- [The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification](https://mlsanity.com/paper/2305.04940/) (2023)
- [VADER: A Human-Evaluated Benchmark for Vulnerability Assessment, Detection, Explanation, and Remediation](https://mlsanity.com/paper/2505.19395/) (2025)
- [Challenging Bug Prediction and Repair Models with Synthetic Bugs](https://mlsanity.com/paper/2310.02407/) (2023)
- [Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection](https://mlsanity.com/paper/2412.12039/) (2024)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [DefenderBench: A Toolkit for Evaluating Language Agents in Cybersecurity Environments](https://mlsanity.com/paper/2506.00739/) (2025)

## Abstract (arXiv)

Data-driven research on the automated discovery and repair of security vulnerabilities in source code requires comprehensive datasets of real-life vulnerable code and their fixes. To assist in such research, we propose a method to automatically collect and curate a comprehensive vulnerability dataset from Common Vulnerabilities and Exposures (CVE) records in the public National Vulnerability Database (NVD). We implement our approach in a fully automated dataset collection tool and share an initial release of the resulting vulnerability dataset named CVEfixes. The CVEfixes collection tool automatically fetches all available CVE records from the NVD, gathers the vulnerable code and corresponding fixes from associated open-source repositories, and organizes the collected information in a relational database. Moreover, the dataset is enriched with meta-data such as programming language, and detailed code and security metrics at five levels of abstraction. The collection can easily be repeated to keep up-to-date with newly discovered or patched vulnerabilities. The initial release of CVEfixes spans all published CVEs up to 9 June 2021, covering 5365 CVE records for 1754 open-source projects that were addressed in a total of 5495 vulnerability fixing commits. CVEfixes supports various types of data-driven software security research, such as vulnerability prediction, vulnerability classification, vulnerability severity prediction, analysis of vulnerability-related code changes, and automated vulnerability repair.