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
Sheng et al. (Tue,) studied this question.