# Exploring the Efficacy of Large Language Models (GPT-4) in Binary Reverse Engineering

arXiv: https://arxiv.org/abs/2406.06637 · HTML: https://mlsanity.com/paper/2406.06637/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries](https://mlsanity.com/paper/2301.01701/) (2023)
- [SoK: Explainable Machine Learning for Computer Security Applications](https://mlsanity.com/paper/2208.10605/) (2022)
- [Pop Quiz! Can a Large Language Model Help With Reverse Engineering?](https://mlsanity.com/paper/2202.01142/) (2022)
- [IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis](https://mlsanity.com/paper/2006.10720/) (2020)

## Built on by (most cited first, 8 of 8)

- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [A Decompilation-Driven Framework for Malware Detection with Large Language Models](https://mlsanity.com/paper/2601.09035/) (2026)
- [Inferring Pluggable Types with Machine Learning](https://mlsanity.com/paper/2406.15676/) (2024)
- [NeuroDeX: Unlocking Diverse Support in Decompiling Deep Neural Network Executables](https://mlsanity.com/paper/2509.06402/) (2025)
- [Exposing Hidden Interfaces: LLM-Guided Type Inference for Reverse Engineering macOS Private Frameworks](https://mlsanity.com/paper/2601.01673/) (2026)
- [Multi-View Decompilation for LLM-Based Malware Classification](https://mlsanity.com/paper/2606.20436/) (2026)
- [Humans vs. Machines: A Comparison With AI Agents in Software Reverse Engineering](https://mlsanity.com/paper/2610.14960/) (2026)

## Abstract (arXiv)

This study investigates the capabilities of Large Language Models (LLMs), specifically GPT-4, in the context of Binary Reverse Engineering (RE). Employing a structured experimental approach, we analyzed the LLM's performance in interpreting and explaining human-written and decompiled codes. The research encompassed two phases: the first on basic code interpretation and the second on more complex malware analysis. Key findings indicate LLMs' proficiency in general code understanding, with varying effectiveness in detailed technical and security analyses. The study underscores the potential and current limitations of LLMs in reverse engineering, revealing crucial insights for future applications and improvements. Also, we examined our experimental methodologies, such as methods of evaluation and data constraints, which provided us with a technical vision for any future research activity in this field.