mlsanityData to 7 Oct 2026

A Study of Vulnerability Repair in JavaScript Programs with Large Language Models

arXiv 2403.13193 ↗First posted 19 Mar 2024cs.CR cs.AIcited by 7

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Abstract (arXiv)

In recent years, JavaScript has become the most widely used programming language, especially in web development. However, writing secure JavaScript code is not trivial, and programmers often make mistakes that lead to security vulnerabilities in web applications. Large Language Models (LLMs) have demonstrated substantial advancements across multiple domains, and their evolving capabilities indicate their potential for automatic code generation based on a required specification, including automatic bug fixing. In this study, we explore the accuracy of LLMs, namely ChatGPT and Bard, in finding and fixing security vulnerabilities in JavaScript programs. We also investigate the impact of context in a prompt on directing LLMs to produce a correct patch of vulnerable JavaScript code. Our experiments on real-world software vulnerabilities show that while LLMs are promising in automatic program repair of JavaScript code, achieving a correct bug fix often requires an appropriate amount of context in the prompt.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

7 of 19 references.

References · 7

  1. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 417
  4. 2022A Systematic Evaluation of Large Language Models of Codecited by 360
  5. 2023Large Language Models for Code: Security Hardening and Adversarial Testingcited by 149
  6. 2022Lost at C: A User Study on the Security Implications of Large Language Model Code Assistantscited by 87
  7. 2023ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patchingcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.

  1. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 70
  2. 2024Agents in Software Engineering: Survey, Landscape, and Visioncited by 60
  3. 2024How Well Do Large Language Models Serve as End-to-End Secure Code Agents for Python?cited by 7
  4. 2025Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Studycited by 2
  5. 2025HiLDe: Intentional Code Generation via Human-in-the-Loop Decodingcited by 1
  6. 2026A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Developmentcited by 0
  7. 2026OpenRTLSet: A Fully Open-Source Dataset for Large Language Model-based Verilog Module Designcited by 0