# Deep Learning-based Binary Analysis for Vulnerability Detection in x86-64 Machine Code

arXiv: https://arxiv.org/abs/2601.09157 · HTML: https://mlsanity.com/paper/2601.09157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Towards Sparse Hierarchical Graph Classifiers](https://mlsanity.com/paper/1811.01287/) (2018)
- [On Provable Benefits of Depth in Training Graph Convolutional Networks](https://mlsanity.com/paper/2110.15174/) (2021)
- [PalmTree: Learning an Assembly Language Model for Instruction Embedding](https://mlsanity.com/paper/2103.03809/) (2021)
- [How secure is AI-generated Code: A Large-Scale Comparison of Large Language Models](https://mlsanity.com/paper/2404.18353/) (2024)
- [Detecting False Alarms from Automatic Static Analysis Tools: How Far are We?](https://mlsanity.com/paper/2202.05982/) (2022)
- [Exploring the Impact of Temperature Scaling in Softmax for Classification and Adversarial Robustness](https://mlsanity.com/paper/2502.20604/) (2025)
- [Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks](https://mlsanity.com/paper/2002.03388/) (2020)
- [Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses](https://mlsanity.com/paper/2311.16396/) (2023)
- [On the Generation of Disassembly Ground Truth and the Evaluation of Disassemblers](https://mlsanity.com/paper/2012.09155/) (2020)

## Abstract (arXiv)

While much of the current research in deep learning-based vulnerability detection relies on disassembled binaries, this paper explores the feasibility of extracting features directly from raw x86-64 machine code. Although assembly language is more interpretable for humans, it requires more complex models to capture token-level context. In contrast, machine code may enable more efficient, lightweight models and preserve all information that might be lost in disassembly. This paper approaches the task of vulnerability detection through an exploratory study on two specific deep learning model architectures and aims to systematically evaluate their performance across three vulnerability types. The results demonstrate that graph-based models consistently outperform sequential models, emphasizing the importance of control flow relationships, and that machine code contains sufficient information for effective vulnerability discovery.