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paperSeptember 2026Unreviewed

Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models

Manit Kaushik, Ishir Bhardwaj, Pranav Gupta, Pankaj Jalote, Arun Balaji Buduru

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Abstract

JavaScript powers approximately 98.8% of all websites, making vulnerabilities in its code a significant security risk, yet existing detection approaches such as Static Application Security Testing (SAST) tools often fail to identify many real-world vulnerabilities when applied to isolated code snippets. This paper presents an empirical study of Large Language Model (LLM)-based vulnerability identification for JavaScript programs, evaluating three LLM families (Gemini 1.5 Flash, GPT-4o Mini, Deep

Categories

Threat Modeling

Cite

@misc{kaushik2026exploring,
  title = {{Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models}},
  author = {Manit Kaushik and Ishir Bhardwaj and Pranav Gupta and Pankaj Jalote and Arun Balaji Buduru},
  year = {2026},
  month = sep,
  eprint = {2609.13816},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.13816}
}