Software Vulnerability Detection Using a Lightweight Graph Neural Network

📰 ArXiv cs.AI

A lightweight graph neural network (GNN) is proposed for software vulnerability detection, achieving performance almost as good as large language models (LLMs) with less computational requirements

advanced Published 1 Apr 2026

Action Steps

Represent code as a graph to capture relational structure
Apply graph neural network (GNN) to learn vulnerability patterns
Train and fine-tune the GNN model using a dataset of labeled vulnerabilities
Evaluate the performance of the GNN model against LLMs and other baselines

Who Needs to Know This

Software engineers and security teams can benefit from this approach to detect vulnerabilities in their codebases more efficiently, while researchers can explore the applications of GNNs in vulnerability detection

Key Insight

💡 Graph neural networks can be an efficient and effective alternative to large language models for software vulnerability detection

Key Takeaways

A lightweight graph neural network (GNN) is proposed for software vulnerability detection, achieving performance almost as good as large language models (LLMs) with less computational requirements

Full Article

Title: Software Vulnerability Detection Using a Lightweight Graph Neural Network

Abstract:
arXiv:2603.29216v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as a popular choice in vulnerability detection studies given their foundational capabilities, open source availability, and variety of models, but have limited scalability due to extensive compute requirements. Using the natural graph relational structure of code, we show that our proposed graph neural network (GNN) based deep learning model VulGNN for vulnerability detection can achieve performance almos

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