Wednesday 26 March 2025
The quest for a reliable decompiler has been an ongoing challenge in the world of computer science. Decompilers are essential tools for analyzing and understanding binary code, but they often struggle to accurately reconstruct source code due to limitations in their design and training data. In recent years, researchers have turned to large language models (LLMs) in hopes of improving decompilation performance, with some promising results.
The latest effort in this area comes from a team of researchers who have developed ReF Decompile, an end-to-end decompiler that leverages LLMs to decompose binary code into readable source code. Unlike previous approaches, which rely on manual rule-based systems or fine-tuning pre-trained models for specific tasks, ReF Decompile uses a combination of relabeling and function call strategies to improve the accuracy and reliability of its output.
The relabeling strategy involves reformatting data by replacing jump target addresses with labels, making it easier for the model to identify control flow structures. This approach is particularly effective in handling optimized binary code, which often employs complex jumps and branches that are difficult for traditional decompilers to accurately reconstruct.
The function call strategy, on the other hand, involves training the model to infer variable types and retrieve missing information from binary files. By interacting with the binary file, the model can fill in gaps in its understanding of the code, leading to more accurate and complete output.
In a series of experiments, the researchers demonstrated the effectiveness of ReF Decompile by comparing it to existing decompilation tools and baselines on various datasets. The results were impressive, with ReF Decompile achieving state-of-the-art performance on several benchmarks and outperforming refine-based models with similar model sizes.
One of the key advantages of ReF Decompile is its ability to handle a wide range of binary code formats and optimization levels, making it a versatile tool for researchers and developers alike. The model’s performance also improved significantly when fine-tuned on specific datasets, suggesting that it has potential applications in areas such as malware analysis and reverse engineering.
While there are still challenges to be overcome, ReF Decompile represents an important step forward in the development of reliable decompilers. By combining LLMs with innovative strategies for relabeling and function calls, researchers have created a tool that is well-equipped to tackle even the most complex binary code.
Cite this article: “ReF Decompile: A Novel End-to-End Decompiler Leveraging Large Language Models”, The Science Archive, 2025.
Decompilation, Large Language Models, Binary Code, Source Code, Decompiler, Relabeling, Function Calls, Optimized Binary Code, Malware Analysis, Reverse Engineering.
