The increasing adoption of Large Language Models (LLMs) in software engineering has sparked interest in their use for software vulnerability detection. However, the rapid development of this field has resulted in a fragmented research landscape, with diverse studies that are difficult to compare due to differences in, e.g., system designs and dataset usage. This fragmentation makes it difficult to obtain a clear overview of the state-of-the-art or compare and categorize studies meaningfully. In this work, we present a comprehensive systematic literature review (SLR) of LLM-based software vulnerability detection. We analyze 263 studies published between January 2020 and November 2025, categorizing them by task formulation, input representation, system architecture, and techniques. Further, we analyze the datasets used, including their characteristics, vulnerability coverage, and diversity. We present a fine-grained taxonomy of vulnerability detection approaches, identify key limitations, and outline actionable future research opportunities. By providing a structured overview of the field, this review improves transparency and serves as a practical guide for researchers and practitioners aiming to conduct more comparable and reproducible research. We publicly release all artifacts and maintain a living repository of LLM-based software vulnerability detection studies at https://github.com/hs-esslingen-it-security/Awesome-LLM4SVD.

翻译：大语言模型在软件工程领域的日益广泛应用，激发了其在软件漏洞检测方面的研究兴趣。然而，该领域的快速发展导致了研究格局的碎片化，众多研究因系统设计、数据集使用等方面的差异而难以进行有效比较。这种碎片化使得难以清晰把握该领域的前沿进展，也难以对研究进行有意义的比较与归类。本研究针对基于大语言模型的软件漏洞检测，进行了一项全面的系统性文献综述。我们分析了2020年1月至2025年11月期间发表的263项研究，并依据任务定义、输入表示、系统架构和技术方法对其进行分类。此外，我们分析了所使用的数据集，包括其特征、漏洞覆盖范围及多样性。我们提出了一个细粒度的漏洞检测方法分类体系，指出了当前研究的主要局限性，并规划了具有可操作性的未来研究方向。通过提供该领域的结构化概览，本综述提升了研究的透明度，并为旨在开展更具可比性和可复现性研究的研究人员与实践者提供了实用指南。我们已公开所有相关资源，并在 https://github.com/hs-esslingen-it-security/Awesome-LLM4SVD 维护一个关于基于大语言模型的软件漏洞检测研究的动态知识库。