# Boosting Neural Networks to Decompile Optimized Binaries

arXiv: https://arxiv.org/abs/2301.00969 · HTML: https://mlsanity.com/paper/2301.00969/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 155 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [DIRE: A Neural Approach to Decompiled Identifier Naming](https://mlsanity.com/paper/1909.09029/) (2019)
- [XDA: Accurate, Robust Disassembly with Transfer Learning](https://mlsanity.com/paper/2010.00770/) (2020)
- [Towards Neural Decompilation](https://mlsanity.com/paper/1905.08325/) (2019)
- [Datalog Disassembly](https://mlsanity.com/paper/1906.03969/) (2019)
- [Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model](https://mlsanity.com/paper/2002.06799/) (2020)

## Built on by (most cited first, 11 of 11)

- [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024)
- [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://mlsanity.com/paper/2305.12520/) (2023)
- [TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment](https://mlsanity.com/paper/2404.11121/) (2024)
- [CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment](https://mlsanity.com/paper/2410.13903/) (2024)
- [Learning Program Behavioral Models from Synthesized Input-Output Pairs](https://mlsanity.com/paper/2407.08597/) (2024)
- [On the Lack of Robustness of Binary Function Similarity Systems](https://mlsanity.com/paper/2412.04163/) (2024)
- [Fast, Fine-Grained Equivalence Checking for Neural Decompilers](https://mlsanity.com/paper/2501.04811/) (2025)
- [Asm2SrcEval: Evaluating Large Language Models for Assembly-to-Source Code Translation](https://mlsanity.com/paper/2512.00134/) (2025)
- [The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers](https://mlsanity.com/paper/2601.02045/) (2026)
- [Multi-View Decompilation for LLM-Based Malware Classification](https://mlsanity.com/paper/2606.20436/) (2026)
- [Echo: Learning-based Matching Decompilation using Trusted Back Translation](https://mlsanity.com/paper/2609.18706/) (2026)

## Abstract (arXiv)

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.