用轻量图神经网络实现高效漏洞检测，性能接近大模型但体积小100倍
Software Vulnerability Detection Using a Lightweight Graph Neural Network
- 基于代码的图结构设计轻量GNN模型VulGNN
- 性能接近LLM，模型大小仅为后者的1/100
- 适合部署在开发流水线中，便于边缘计算
大型语言模型（LLMs）在漏洞检测中因基础能力强大、开源可得而广受青睐，但受限于计算资源需求大，难以规模化。利用代码天然的图结构关系，我们提出基于图神经网络（GNN）的深度学习模型VulGNN，其漏洞检测性能几乎与LLM相当，但模型尺寸小100倍，且训练和定制速度快。本文详述VulGNN架构，开展组件消融实验、学习率分析，并验证其在不同代码数据集上的泛化能力。作为轻量级漏洞分析模型，VulGNN具备高效率与可部署性，适用于真实软件开发流程中的边缘场景。
原文摘要 · Abstract (English)
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 almost on par with LLMs, but is 100 times smaller in size and fast to retrain and customize. We describe the VulGNN architecture, ablation studies on components, learning rates, and generalizability to different code datasets. As a lightweight model for vulnerability analysis, VulGNN is efficient and deployable at the edge as part of real-world software development pipelines.
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