# LineVD: Statement-level Vulnerability Detection using Graph Neural Networks

arXiv: https://arxiv.org/abs/2203.05181 · HTML: https://mlsanity.com/paper/2203.05181/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2022.

## Built on by (most cited first, 30 of 35)

- [An Empirical Study of Deep Learning Models for Vulnerability Detection](https://mlsanity.com/paper/2212.08109/) (2022)
- [An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection](https://mlsanity.com/paper/2406.06822/) (2024)
- [LLMDFA: Analyzing Dataflow in Code with Large Language Models](https://mlsanity.com/paper/2402.10754/) (2024)
- [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)
- [Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2406.05892/) (2024)
- [Towards Causal Deep Learning for Vulnerability Detection](https://mlsanity.com/paper/2310.07958/) (2023)
- [Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT](https://mlsanity.com/paper/2404.17110/) (2024)
- [When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection](https://mlsanity.com/paper/2308.10523/) (2023)
- [A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2507.22659/) (2025)
- [Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++](https://mlsanity.com/paper/2407.17053/) (2024)
- [Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?](https://mlsanity.com/paper/2407.17803/) (2024)
- [An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph](https://mlsanity.com/paper/2304.11072/) (2023)
- [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024)
- [XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection](https://mlsanity.com/paper/2309.14677/) (2023)
- [Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection](https://mlsanity.com/paper/2412.12039/) (2024)
- [LPASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs](https://mlsanity.com/paper/2505.24451/) (2025)
- [SCoPE: Evaluating LLMs for Software Vulnerability Detection](https://mlsanity.com/paper/2407.14372/) (2024)
- [EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection](https://mlsanity.com/paper/2407.19216/) (2024)
- [ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data](https://mlsanity.com/paper/2408.16028/) (2024)
- [MVD: A Multi-Lingual Software Vulnerability Detection Framework](https://mlsanity.com/paper/2412.06166/) (2024)
- [Using ML filters to help automated vulnerability repairs: when it helps and when it doesn't](https://mlsanity.com/paper/2504.07027/) (2025)
- [Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities](https://mlsanity.com/paper/2306.06109/) (2023)
- [Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors](https://mlsanity.com/paper/2401.02686/) (2024)
- [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)
- [Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points](https://mlsanity.com/paper/2412.11194/) (2024)
- [A Study on Mixup-Inspired Augmentation Methods for Software Vulnerability Detection](https://mlsanity.com/paper/2504.15632/) (2025)
- [A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants](https://mlsanity.com/paper/2505.23875/) (2025)
- [Evaluating Line-level Localization Ability of Learning-based Code Vulnerability Detection Models](https://mlsanity.com/paper/2510.11202/) (2025)

## Abstract (arXiv)

Current machine-learning based software vulnerability detection methods are primarily conducted at the function-level. However, a key limitation of these methods is that they do not indicate the specific lines of code contributing to vulnerabilities. This limits the ability of developers to efficiently inspect and interpret the predictions from a learnt model, which is crucial for integrating machine-learning based tools into the software development workflow. Graph-based models have shown promising performance in function-level vulnerability detection, but their capability for statement-level vulnerability detection has not been extensively explored. While interpreting function-level predictions through explainable AI is one promising direction, we herein consider the statement-level software vulnerability detection task from a fully supervised learning perspective. We propose a novel deep learning framework, LineVD, which formulates statement-level vulnerability detection as a node classification task. LineVD leverages control and data dependencies between statements using graph neural networks, and a transformer-based model to encode the raw source code tokens. In particular, by addressing the conflicting outputs between function-level and statement-level information, LineVD significantly improve the prediction performance without vulnerability status for function code. We have conducted extensive experiments against a large-scale collection of real-world C/C++ vulnerabilities obtained from multiple real-world projects, and demonstrate an increase of 105% in F1-score over the current state-of-the-art.