arXiv2026
Reverse engineering (RE) is central to cybersecurity, supporting tasks such as decompilation, deobfuscation, and security analysis. However, RE remains labor-intensive and expertise-demanding, as analysts often manually recover high-level semantics from low-level program representations. Recent advances in large language models (LLMs) provide a promising way to address these challenges through program artifact understanding, semantic reasoning, and tool-augmented problem solving. This potential has stimulated interest in LLM-assisted RE, but existing studies remain scattered across different tasks, targets, methodologies, and evaluation practices. Despite these advances, the literature still lacks a comprehensive survey that consolidates progress, systematizes technical choices, and clarifies open challenges and future opportunities. To fill this gap, we present a systematic survey of LLM-assisted RE, covering 48 peer-reviewed and published research articles identified through our search and selection protocol as of July 1, 2026. We develop a faceted taxonomy that organizes prior studies along six dimensions: task objective, analysis target, methodological approach, evaluation protocol, training scale, and data quality. We extract task formulations and experimental settings from existing studies to support comparison, reproducibility, and future research. From this review, we synthesize research gaps, characterize key challenges, and outline future directions toward more reliable, reproducible, and security-relevant applications of LLMs in RE.