mlsanityData to 14 Oct 2026

ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

arXiv 2110.07317 ↗First posted 14 Oct 2021cs.LG cs.CRcited by 32

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Abstract (arXiv)

Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks. It, however, is also a challenging step that requires specialized expertise in security and code representation. To this end, we aim to develop a general, practical, and programming language-independent model capable of running on various source codes and libraries without difficulty. Therefore, we consider vulnerability detection as an inductive text classification problem and propose ReGVD, a simple yet effective graph neural network-based model for the problem. In particular, ReGVD views each raw source code as a flat sequence of tokens to build a graph, wherein node features are initialized by only the token embedding layer of a pre-trained programming language (PL) model. ReGVD then leverages residual connection among GNN layers and examines a mixture of graph-level sum and max poolings to return a graph embedding for the source code. ReGVD outperforms the existing state-of-the-art models and obtains the highest accuracy on the real-world benchmark dataset from CodeXGLUE for vulnerability detection. Our code is available at: urlhttps://github.com/daiquocnguyen/GNN-ReGVD.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

24 of 33 references.

References · 24

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12 more
  1. 2021CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generationcited by 744
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 5; 2024: 10; 2025: 8; 2026: 5.

32 papers.

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  1. 2022An Empirical Study of Deep Learning Models for Vulnerability Detectioncited by 46
  2. 2024Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuningcited by 30
  3. 2022Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detectioncited by 19
  4. 2024Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanationcited by 12
  5. 2024Investigating Large Language Models for Code Vulnerability Detection: An Experimental Studycited by 12
  6. 2023When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detectioncited by 8
  7. 2025VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detectioncited by 8
  8. 2023Just-in-Time Detection of Silent Security Patchescited by 7
  9. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  10. 2024Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++cited by 6
  11. 2023An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graphcited by 5
  12. 2024A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Taskscited by 5
20 more
  1. 2025Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Predictioncited by 5
  2. 2026Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industrycited by 5
  3. 2023XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detectioncited by 4
  4. 2022Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive Learningcited by 2
  5. 2022On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processingcited by 2
  6. 2024EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learningcited by 2
  7. 2024MVD: A Multi-Lingual Software Vulnerability Detection Frameworkcited by 1
  8. 2023Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilitiescited by 0
  9. 2024Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysiscited by 0
  10. 2024Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Pointscited by 0
  11. 2025MFH: A Multi-faceted Heuristic Algorithm Selection Approach for Software Verificationcited by 0
  12. 2025A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variantscited by 0
  13. 2025Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approachescited by 0
  14. 2025Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimizationcited by 0
  15. 2025Weakly Supervised Vulnerability Localization via Multiple Instance Learningcited by 0
  16. 2025MulVuln: Enhancing Pre-trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detectioncited by 0
  17. 2026Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Trainingcited by 0
  18. 2026Software Vulnerability Detection Using a Lightweight Graph Neural Networkcited by 0
  19. 2026PLMGH: What Matters in PLM-GNN Hybrids for Code Classification and Vulnerability Detectioncited by 0
  20. 2026Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networkscited by 0