# MVD: Memory-Related Vulnerability Detection Based on Flow-Sensitive Graph Neural Networks

arXiv: https://arxiv.org/abs/2203.02660 · HTML: https://mlsanity.com/paper/2203.02660/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2022.

## Built on by (most cited first, 12 of 12)

- [An Empirical Study of Deep Learning Models for Vulnerability Detection](https://mlsanity.com/paper/2212.08109/) (2022)
- [Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection](https://mlsanity.com/paper/2212.08108/) (2022)
- [Correctness Assessment of Code Generated by Large Language Models Using Internal Representations](https://mlsanity.com/paper/2501.12934/) (2025)
- [Towards Causal Deep Learning for Vulnerability Detection](https://mlsanity.com/paper/2310.07958/) (2023)
- [When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection](https://mlsanity.com/paper/2308.10523/) (2023)
- [XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection](https://mlsanity.com/paper/2309.14677/) (2023)
- [Vulnerability Detection in C/C++ Code with Deep Learning](https://mlsanity.com/paper/2405.12384/) (2024)
- [Learning-based Models for Vulnerability Detection: An Extensive Study](https://mlsanity.com/paper/2408.07526/) (2024)
- [CLNX: Bridging Code and Natural Language for C/C++ Vulnerability-Contributing Commits Identification](https://mlsanity.com/paper/2409.07407/) (2024)
- [Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points](https://mlsanity.com/paper/2412.11194/) (2024)
- [Software Vulnerability Detection Using a Lightweight Graph Neural Network](https://mlsanity.com/paper/2603.29216/) (2026)
- [Knowledge-Enhanced Agentic Vulnerability Repair](https://mlsanity.com/paper/2607.00820/) (2026)

## Abstract (arXiv)

Memory-related vulnerabilities constitute severe threats to the security of modern software. Despite the success of deep learning-based approaches to generic vulnerability detection, they are still limited by the underutilization of flow information when applied for detecting memory-related vulnerabilities, leading to high false positives. In this paper,we propose MVD, a statement-level Memory-related Vulnerability Detection approach based on flow-sensitive graph neural networks (FS-GNN). FS-GNN is employed to jointly embed both unstructured information (i.e., source code) and structured information (i.e., control- and data-flow) to capture implicit memory-related vulnerability patterns. We evaluate MVD on the dataset which contains 4,353 real-world memory-related vulnerabilities, and compare our approach with three state-of-the-art deep learning-based approaches as well as five popular static analysisbased memory detectors. The experiment results show that MVD achieves better detection accuracy, outperforming both state-of-theart DL-based and static analysis-based approaches. Furthermore, MVD makes a great trade-off between accuracy and efficiency.