基于异构图神经网络的PHP污点型漏洞检测方法OA

PHP's lack of built-in validation functions and user-interactive nature leads to widespread taint-style vulnerabilities.While graph neural networks improve detection accuracy,code property graphs often introduce redundancy,resulting in high false positives/negatives.For this purpose,a PHP taint-style vulnerability detection method named HG-VulD(Heterogeneous Graph-Vulnerability Detection)is proposed,which is based on sub-property graphs and heterogeneous graph neural networks.Sub-Property Graph(SPG)retains vulnerability-relevant semantics and structure by reverse traversal(sinks to sources),removing irrelevant nodes to reduce complexity.HG-VulD encodes code nodes via semantic and type features,using Heterogeneous Graph Neural Networks(HGNN)to independently learn syntax(Abstract Syntax Tree,AST),control flow(Control Flow Graph,CFG),and dependency information(Program Dependence Graph,PDG),with attention-based edge aggregation enhancing classification.Evaluations on a 260 k-file synthetic dataset show 96.05％F1,surpassing RIPS,WAP,and VulEye.Real-world tests achieve 73.82％(XSS)and 67.81％(SQLI)accuracy,demonstrating practical generalization.