PatchEval: A New Benchmark for Evaluating LLMs on Patching Real-World Vulnerabilities
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Updated
Aug 31, 2026 - Python
PatchEval: A New Benchmark for Evaluating LLMs on Patching Real-World Vulnerabilities
[ASE 2026] PSearch: Search-based Patch Generation in the Era of LLM-based Automated Program Repair
For our ISSTA23 paper "How Effective are Neural Networks for Fixing Security Vulnerabilities?" by Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier, Jordan Davis, Lin Tan, Petr Babkin, and Sameena Shah.
AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities
[2023 TDSC] Pre-trained Model-based Automated Software Vulnerability Repair: How Far are We?
An LLM-based model for vulnerability patch generation in C/C++ source code
Defensive Python security model, 72K corpus, and reproducible vulnerability-repair evaluation
Generating Vulnerability Security Fixes with Code Language Models
"Multi-agent LLM system achieving 99.4% success rate on automated security vulnerability repair across 8 CWE categories"
Tool for collecting vulnerability-fix pairs from GitHub Security Advisories, developed for a B.Sc. thesis on vulnerability repair benchmarking.
Journal-grade reproducibility artifact for multi-agent vulnerability repair
🔍 Predict software vulnerability trends using Multi-Recurrent Neural Networks for improved cybersecurity strategies and informed decision-making.
Premium SFT dataset of verified Python security repairs (L4/L5) for AI coding agents & DevSecOps Copilots.
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