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Towards Neural Decompilation
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We address the problem of automatic decompilation, converting a program in low-level representation back to a higher-level human-readable programming language. The problem of decompilation is extremely important for security researchers. Finding vulnerabilities and understanding how malware operates is much easier when done over source code. The importance of decompilation has motivated the construction of hand-crafted rule-based decompilers. Such decompilers have been designed by experts to detect specific control-flow structures and idioms in low-level code and lift them to source level. The cost of supporting additional languages or new language features in these models is very high. We present a novel approach to decompilation based on neural machine translation. The main idea is to automatically learn a decompiler from a given compiler. Given a compiler from a source language S to a target language T , our approach automatically trains a decompiler that can translate (decompile) T back to S . We used our framework to decompile both LLVM IR and x86 assembly to C code with high success rates. Using our LLVM and x86 instantiations, we were able to successfully decompile over 97% and 88% of our benchmarks respectively.
Forward citations
Cited by 4 Pith papers
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D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
D-LIFT uses a composite quality score, D-Score, as a reinforcement-learning reward to fine-tune LLMs that rewrite decompiled code, claiming large gains in readability without losing accuracy.
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The Strengths and Behavioral Quirks of Java Bytecode Decompilers
No single Java decompiler produces recompilable, behavior-preserving source code for more than 84% (syntactic) and 78% (test-based semantic) of the studied classes, while a naive multi-decompiler ensemble reaches 95% and 91%.
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Context-Guided Decompilation: A Step Towards Re-executability
ICL4Decomp applies in-context learning to guide LLMs in generating re-executable decompiled code from binaries, reporting roughly 40% higher re-executability than prior methods across datasets and optimization levels.
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Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation
On legacy MUMPS code, LLM-generated line comments are rated almost as good as human-written ones; on IBM assembly they are rated low, and standard automated metrics do not predict human quality scores.
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