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The significance of early vulnerability identification in ensuring security during software development cannot be denied. In this research, we introduce CWEpredBELL, a unique automated vulnerability prediction method that makes use of a modified pre-trained language model derived from CodeBERT. With a binary classification layer, an improved optimizer, and a fine-tuned loss function to boost model performance, our method is especially tailored for identifying vulnerabilities in source code. We used cross-validation techniques and the Local Interpretable Model-Agnostic Explanations (LIME) approach to identify particular lines of error in the source code. The experimental comparison demonstrates that CWEpredBELL is an effective method of automatically identifying vulnerabilities.

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Improving Software Security Through a LLM-Based Vulnerability Detection Model

  • 会议论文

  • 发表日期：

    2026年

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  • Syeda Sadia Alam,
  • Mst Shapna Akter,
  • Alfredo Cuzzocrea

摘要

The significance of early vulnerability identification in ensuring security during software development cannot be denied. In this research, we introduce CWEpredBELL, a unique automated vulnerability prediction method that makes use of a modified pre-trained language model derived from CodeBERT. With a binary classification layer, an improved optimizer, and a fine-tuned loss function to boost model performance, our method is especially tailored for identifying vulnerabilities in source code. We used cross-validation techniques and the Local Interpretable Model-Agnostic Explanations (LIME) approach to identify particular lines of error in the source code. The experimental comparison demonstrates that CWEpredBELL is an effective method of automatically identifying vulnerabilities.