# Learning to Repair Software Vulnerabilities with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1805.07475 · HTML: https://mlsanity.com/paper/1805.07475/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2018.

## Built on by (most cited first, 12 of 12)

- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [Neural Transfer Learning for Repairing Security Vulnerabilities in C Code](https://mlsanity.com/paper/2104.08308/) (2021)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python](https://mlsanity.com/paper/2201.08441/) (2022)
- [Enhanced Automated Code Vulnerability Repair using Large Language Models](https://mlsanity.com/paper/2401.03741/) (2024)
- [Learning to Make Compiler Optimizations More Effective](https://mlsanity.com/paper/2102.13514/) (2021)
- [Towards Making Deep Learning-based Vulnerability Detectors Robust](https://mlsanity.com/paper/2108.00669/) (2021)
- [Evolving GAN Formulations for Higher Quality Image Synthesis](https://mlsanity.com/paper/2102.08578/) (2021)
- [LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types](https://mlsanity.com/paper/2306.06935/) (2023)
- [Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods](https://mlsanity.com/paper/2202.03055/) (2022)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/2602.23121/) (2026)
- [Catching Developers in the Flow: Low-Latency Agentic Program Repair at Google Scale](https://mlsanity.com/paper/2610.07289/) (2026)

## Abstract (arXiv)

Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections. We demonstrate that the proposed adversarial learning approach is an effective technique for repairing software vulnerabilities, performing close to seq2seq approaches that require labeled pairs. The proposed Generative Adversarial Network approach is application-agnostic in that it can be applied to other problems similar to code repair, such as grammar correction or sentiment translation.