📝 SummarXiv

Abstract

Reverse Engineering (RE) is central to software security, enabling tasks such as vulnerability discovery and malware analysis, but it remains labor-intensive and requires substantial expertise. Earlier advances in deep learning start to automate parts of RE, particularly for malware detection and vulnerability classification. More recently, a rapidly growing body of work has applied Large Language Models (LLMs) to similar purposes. Their role compared to prior machine learning remains unclear, since some efforts simply adapt existing pipelines with minimal change while others seek to exploit broader reasoning and generative abilities. These differences, combined with varied problem definitions, methods, and evaluation practices, limit comparability, reproducibility, and cumulative progress. This paper systematizes the field by reviewing 44 research papers, including peer-reviewed publications and preprints, and 18 additional open-source projects that apply LLMs in RE. We propose a taxonomy that organizes existing work by objective, target, method, evaluation strategy, and data scale. Our analysis identifies strengths and limitations, highlights reproducibility and evaluation gaps, and examines emerging risks. We conclude with open challenges and future research directions that aim to guide more coherent and security-relevant applications of LLMs in RE.