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

The research paper by Pordanesh and Tan explores the potential and limitations of GPT-4 in binary reverse engineering, finding that while it excels in basic code interpretation, its performance is inconsistent in more complex tasks, highlighting the need for specialized training and evaluation methods in this domain.

Abstract

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.

Method

The authors of the research paper utilized a structured experimental methodology to evaluate the capabilities of GPT-4 in binary reverse engineering. They employed two datasets—one with simple C programming problems and another with malware source codes from GitHub—which were processed using decompiling tools like Ghidra and RetDec. The study was conducted in two phases, with the first phase assessing GPT-4's interpretation of original, stripped, and decompiled code using the BLEU score for evaluation. The second phase involved more complex scenarios, including renaming functions and variables, answering binary questions about code attributes, and providing detailed analyses of decompiled code, all evaluated manually using rubrics. The authors acknowledged the limitations of their methods, particularly the BLEU score's limitations for technical content and the differences between original and decompiled code, and suggested future research should focus on developing more accurate assessment tools and using a wider variety of code sources, including expert-reviewed decompiled code as benchmarks.

Main Finding

The authors' discoveries revealed that GPT-4 is adept at interpreting and explaining human-written code but encounters challenges with decompiled code and in-depth technical and security analyses. The initial phase's BLEU score evaluations were insufficient for meaningful comparisons, prompting a switch to manual evaluations in the second phase. Here, GPT-4 showed promise in renaming functions and variables in decompiled code but struggled with logical connections within the code. It had mixed success in answering binary questions about code attributes and provided general reverse engineering explanations but faltered in detailed technical and security analyses. The findings suggest that while GPT-4 can support reverse engineering efforts, it still requires human oversight for complex tasks, and there is a need for specialized training and better evaluation tools to advance its capabilities in this field.

Conclusion

The conclusion of the research conducted by Saman Pordanesh and Dr. Benjamin Tan is that while Large Language Models (LLMs), specifically GPT-4, show potential in assisting with binary reverse engineering tasks, they currently have limitations, particularly in handling complex code analyses and security-related aspects. The study found that GPT-4 can provide explanations for simple code and has some capability in interpreting decompiled code, but its performance is inconsistent when dealing with more intricate code structures and security vulnerabilities. The authors suggest that future improvements in LLM capabilities for reverse engineering applications will require specialized training datasets and more sophisticated evaluation tools. They also recommend that human expertise should continue to play a significant role in overseeing the application of LLMs in this domain.

Keywords

Large Language Models (LLMs), GPT-4, Binary Reverse Engineering, Code Interpretation, Malware Analysis, Decompiled Code Analysis, AI-Assisted Reverse Engineering, Natural Language Processing (NLP), Evaluation Methods, Data Constraints, Experimental Methodologies, Ghidra, RetDec, BLEU Score, Dataset Composition, Decompilation Process, Scenario Design, Evaluation, Manual Assessment, Rubric-Based Evaluation, Questionnaire, Reverse Engineering-Related Short Answer Questions, Primary Functionality, Key Functions Description, Role of Selected Variable, Error Handling Mechanism, Flow of Execution