Vul-CTG: A Multimodal Framework for Software Vulnerability Detection via Code Text and Graph Integration

Vul-CTG is a new AI framework for detecting software vulnerabilities (bugs that create security weaknesses) by combining two approaches: PLMs (pretrained language models, AI systems trained on large amounts of text) and GNNs (graph neural networks, AI systems that analyze connected data structures). The framework improves on existing methods by better combining code text analysis with program graph analysis, using contrastive learning (training the AI to recognize similarities and differences) and handling unreliable training labels, achieving about 3% better accuracy than previous approaches.