LLM-Based Approach for Buffer Overflow Detection in Source Code

In today’s high-risk environments, software security is critical for companies, as vulnerabilities can expose sensitive information. Robust cybersecurity measures are essential to reducing the risk of unauthorized access. One major security issue is CWE-120, which deals with buffer overflow vulnerabilities. This paper explores the use of automated tools for detecting such vulnerabilities. Traditional detection tools, which often rely on conventional computational models, struggle to identify complex, context-dependent flaws in source code. In this study, we propose an LLM-based method for detecting CWE-120 vulnerabilities. By integrating techniques like dropout and dimensionality reduction, our model improves speed and efficiency. The proposed approach achieved an accuracy of 0.90 and a precision rate of 0.91, demonstrating strong potential for early detection of vulnerabilities, particularly those related to CWE-120, during the software development process.