LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights

Abstract

Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection. Traditional methods, including static and dynamic analysis, face limitations in efficiency, false-positive rates, and scalability with modern software complexity. Through code structure analysis, pattern identification, and repair suggestion generation, LLMs demonstrate a novel approach to vulnerability mitigation. This survey examines LLMs in vulnerability detection, analyzing problem formulation, model selection, application methodologies, datasets, and evaluation metrics. We investigate current research challenges, emphasizing cross-language detection, multimodal integration, and repository-level analysis. Based on our findings, we propose solutions addressing dataset scalability, model interpretability, and low-resource scenarios. Our contributions include: (1) a systematic analysis of LLM applications in vulnerability detection; (2) a unified framework examining patterns and variations across studies; and (3) identification of key challenges and research directions. This work advances the understanding of LLM-based vulnerability detection. The latest findings are maintained at https://github.com/OwenSanzas/LLM-For-Vulnerability-Detection