AI-Powered Vulnerability Detection in Code Using BERT-Based LLM with Transparency Measures

Abstract

Vulnerabilities in source code are major risk in software-intensive systems, making their effective detection essential. Artificial Intelligence (AI) supports this process by analyzing large datasets and identifying threat patterns. However, AI-based methods face challenges in handling big data and understanding context. This research introduces a novel approach to enhance transparency in vulnerability detection using BERT-based Large Language Models (LLMs), integrated with eXplainable AI (XAI) techniques like SHAP, LIME, and attention heatmaps. This architecture ensures transparency throughout the model's lifecycle. An experiment on a large source code dataset achieved 85% accuracy, with XAI tools highlighting influential tokens such as “vulnerable,” “function,” “mysql_tmpdir_list,” and “strmov.” Attention heatmaps also provided insights into token-level interactions, improving the interpretability of the model's decisions.