# End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

arXiv: https://arxiv.org/abs/1703.02458 · HTML: https://mlsanity.com/paper/1703.02458/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 10 of 10)

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- [LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights](https://mlsanity.com/paper/2502.07049/) (2025)
- [Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?](https://mlsanity.com/paper/2306.01754/) (2023)
- [Learning to Reduce False Positives in Analytic Bug Detectors](https://mlsanity.com/paper/2203.09907/) (2022)
- [Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation](https://mlsanity.com/paper/2109.03341/) (2021)
- [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024)
- [Data Quality Issues in Vulnerability Detection Datasets](https://mlsanity.com/paper/2410.06030/) (2024)
- [Learning to Make Compiler Optimizations More Effective](https://mlsanity.com/paper/2102.13514/) (2021)
- [XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection](https://mlsanity.com/paper/2309.14677/) (2023)

## Abstract (arXiv)

Detecting buffer overruns from a source code is one of the most common and yet challenging tasks in program analysis. Current approaches have mainly relied on rigid rules and handcrafted features devised by a few experts, limiting themselves in terms of flexible applicability and robustness due to diverse bug patterns and characteristics existing in sophisticated real-world software programs. In this paper, we propose a novel, data-driven approach that is completely end-to-end without requiring any hand-crafted features, thus free from any program language-specific structural limitations. In particular, our approach leverages a recently proposed neural network model called memory networks that have shown the state-of-the-art performances mainly in question-answering tasks. Our experimental results using source codes demonstrate that our proposed model is capable of accurately detecting simple buffer overruns. We also present in-depth analyses on how a memory network can learn to understand the semantics in programming languages solely from raw source codes, such as tracing variables of interest, identifying numerical values, and performing their quantitative comparisons.