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Large language models (LLMs) are increasingly influential in advancing NLP technology and solving complex tasks, yet their potential misuse in cybersecurity poses significant risks. This paper addresses the challenge of detecting malicious webpages using LLMs, an area with limited research. We evaluate LLMs by expanding zero-shot and few-shot query formulations, testing previously unassessed open-source and proprietary models, and assessing robustness under adversarial conditions. Additionally, we verify model performance using Chain of Thought reasoning and compare these explanations with traditional methods. Our work aims to enhance the application of LLMs in cybersecurity, guiding the development of more effective detection systems.

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An Empirical Assessment of LLM-Based Approaches to Malicious Webpage Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Gracjan Mak,
  • Mateusz Gniewkowski,
  • Paweł Walkowiak,
  • Arkadiusz Janz

摘要

Large language models (LLMs) are increasingly influential in advancing NLP technology and solving complex tasks, yet their potential misuse in cybersecurity poses significant risks. This paper addresses the challenge of detecting malicious webpages using LLMs, an area with limited research. We evaluate LLMs by expanding zero-shot and few-shot query formulations, testing previously unassessed open-source and proprietary models, and assessing robustness under adversarial conditions. Additionally, we verify model performance using Chain of Thought reasoning and compare these explanations with traditional methods. Our work aims to enhance the application of LLMs in cybersecurity, guiding the development of more effective detection systems.