A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models

TL;DR

This systematic literature review comprehensively analyzes 263 studies on LLM-based software vulnerability detection, categorizing methodologies, datasets, and limitations to provide a structured overview and guide future research in this rapidly evolving field.

Contribution

The paper offers the first detailed taxonomy and analysis of LLM-based vulnerability detection studies, addressing fragmentation and promoting reproducibility.

Findings

Identified key limitations in current approaches

Developed a fine-grained taxonomy of methods

Provided actionable future research directions

Abstract

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.