# Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?

arXiv: https://arxiv.org/abs/2412.07538 · HTML: https://mlsanity.com/paper/2412.07538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023)
- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [Deep Learning based Vulnerability Detection: Are We There Yet?](https://mlsanity.com/paper/2009.07235/) (2020)
- [DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection](https://mlsanity.com/paper/2304.00409/) (2023)
- [Does BLEU Score Work for Code Migration?](https://mlsanity.com/paper/1906.04903/) (2019)
- [VulDeeLocator: A Deep Learning-based Fine-grained Vulnerability Detector](https://mlsanity.com/paper/2001.02350/) (2020)
- [Beyond the C: Retargetable Decompilation using Neural Machine Translation](https://mlsanity.com/paper/2212.08950/) (2022)
- [The Clever Hans Effect in Anomaly Detection](https://mlsanity.com/paper/2006.10609/) (2020)
- [Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators](https://mlsanity.com/paper/2212.06008/) (2022)
- [Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks](https://mlsanity.com/paper/2002.03388/) (2020)
- [Automating Code-Related Tasks Through Transformers: The Impact of Pre-training](https://mlsanity.com/paper/2302.04048/) (2023)
- [Deep-Learning-based Vulnerability Detection in Binary Executables](https://mlsanity.com/paper/2212.01254/) (2022)
- [Stack-based Buffer Overflow Detection using Recurrent Neural Networks](https://mlsanity.com/paper/2012.15116/) (2020)

## Abstract (arXiv)

Vulnerability prediction is valuable in identifying security issues efficiently, even though it requires the source code of the target software system, which is a restrictive hypothesis. This paper presents an experimental study to predict vulnerabilities in binary code without source code or complex representations of the binary, leveraging the pivotal idea of decompiling the binary file through neural decompilation and predicting vulnerabilities through deep learning on the decompiled source code. The results outperform the state-of-the-art in both neural decompilation and vulnerability prediction, showing that it is possible to identify vulnerable programs with this approach concerning bi-class (vulnerable/non-vulnerable) and multi-class (type of vulnerability) analysis.