Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models
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, DeepSeek-R1-Distill-Llama-8B) across multiple prompting strategies (zero-shot, chain-of-thought, few-shot) and fine-tuning approaches on a dataset of 1,125 JavaScript code snippets spanning five Common...
| Authors | Manit Kaushik, Ishir Bhardwaj, Pranav Gupta et al. |
|---|---|
| Primary category | cs.CR |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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