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LLM4Decompile: Open-source Large Language Models for Decompilation with Emphasis on Code Executability and Recompilability
Decompilation is crucial in software reverse engineering, aiding in the analysis and understanding of binary executables when their source code is inaccessible. This is valuable for software security analysis, bug detection, and legacy code recovery.
Challenges in Traditional Decompilation Techniques
Traditional decompilation techniques often struggle to produce human-readable and semantically accurate source code, posing a significant challenge. Tools like Ghidra and IDA Pro excel in specific scenarios but may need revisions to restore code to a state easily understandable by humans.
Introduction of LLM4Decompile
LLM4Decompile, developed by researchers from the Southern University of Science and Technology and the Hong Kong Polytechnic University, stands out for its unique approach. It utilizes pre-trained language models (LLMs) to reconstruct accurate and syntactically correct source code from binary executables.
Key Features of LLM4Decompile
LLM4Decompile prioritizes code executability, aiming to produce code that resembles the source in syntax and retains its executable essence. The model has been extensively trained on a dataset of 4 billion tokens, encompassing a wide range of C and assembly code pairs, to imbue it with a deep understanding of code structure and semantics.
Evaluation of LLM4Decompile
LLM4Decompile achieved a significant milestone, demonstrating the ability to accurately decompile binary code with a 90% re-compilability rate and a 21% re-executability rate for its 6B model. This marks a 50% improvement in decompilation performance over its predecessor, GPT-4.
Impact and Future Prospects
The introduction of LLM4Decompile addresses longstanding challenges in decompilation and paves the way for new avenues of research and development. With its advanced methodology and impressive performance, LLM4Decompile heralds a future where decompilation can be as nuanced and refined as the code it seeks to unravel.
For more information, check out the Paper and Github.
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