AI-Driven Open Source Component Security Analysis and Intelligent Vulnerability Prediction Model

Since existing open source component security analysis methods generally rely on static rules and manual features, it is difficult to effectively capture the deep semantics and complex dependencies of the code, resulting in high false positive rate and poor generalization ability in vulnerability detection. This paper introduces the technical path of combining graph neural network with pre-trained code language GraphCodeBERT model to build an intelligent vulnerability prediction model for open source components. By parsing the source code into a code property graph (CPG) that integrates the Abstract Syntax Tree (AST), Control Flow Graph (CFG) and Data Flow Graph (DFG), the structure and semantic information of the code are fully extracted; GraphCodeBERT is used to semantically embed the nodes in the graph to enhance the understanding of the code context; then the embedded graph is input into the graph neural network to learn the structural relationship and information propagation mode between nodes, and extract the feature representation that distinguishes safe and unsafe code; finally, the trained model is used to perform security analysis on new open source components to predict their potential vulnerability risks. Experiments conducted on multiple open source projects show that the F1-score of this method reaches 86% in vulnerability detection tasks. In crossproject and cross-language scenarios, the F1-score of the model in the Juliet→Vul4J task is 0.812. The F1 score is increased to 85.4% when graph structure and semantic embedding are collaboratively modeled. This study provides effective technical support for the security protection of the open source software supply chain and improves the accuracy and practicality of vulnerability detection.