A Neural-based Program Decompiler

TL;DR

This paper introduces Coda, a neural-based framework for binary decompilation that significantly improves accuracy over traditional methods by generating high-level code from binaries using an end-to-end approach.

Contribution

Coda is the first neural-based, end-to-end decompiler that effectively captures program semantics and outperforms existing tools in accuracy and generalization.

Findings

Coda achieves 82% program recovery accuracy on unseen binaries.

Coda outperforms state-of-the-art decompilers with 70% higher accuracy.

Coda effectively preserves program semantics and correctness.

Abstract

Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial advantages. On the other hand, binary decompilation can be leveraged for code vulnerability analysis and malware detection. However, efficient binary decompilation is challenging. Conventional decompilers have the following major limitations: (i) they are only applicable to specific source-target language pair, hence incurs undesired development cost for new language tasks; (ii) their output high-level code cannot effectively preserve the correct functionality of the input binary; (iii) their output program does not capture the semantics of the input and the reversed program is hard to interpret. To address the above problems, we propose Coda, the first end-to-end neural-based framework for code decompilation. Coda decomposes the decompilation task into two key phases: First, Coda employs an instruction type-aware encoder and a tree decoder for generating an abstract syntax tree (AST) with attention feeding during the code sketch generation stage. Second, Coda then updates the code sketch using an iterative error correction machine guided by an ensembled neural error predictor. By finding a good approximate candidate and then fixing it towards perfect, Coda achieves superior performance compared to baseline approaches. We assess Coda's performance with extensive experiments on various benchmarks. Evaluation results show that Coda achieves an average of 82\% program recovery accuracy on unseen binary samples, where the state-of-the-art decompilers yield 0\% accuracy. Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70\% program accuracy.