The paper shows that heterogeneous graph attention networks can classify vulnerable components in real SBOMs at 91% accuracy and that a simple MLP can predict documented multi-vulnerability chains with 0.93 ROC-AUC.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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VulGD is a dynamic open-access graph database that aggregates vulnerability data from multiple sources and uses LLM embeddings to enable more accurate risk assessment and threat prioritization.
citing papers explorer
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Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs
The paper shows that heterogeneous graph attention networks can classify vulnerable components in real SBOMs at 91% accuracy and that a simple MLP can predict documented multi-vulnerability chains with 0.93 ROC-AUC.
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VulGD: A LLM-Powered Dynamic Open-Access Vulnerability Graph Database
VulGD is a dynamic open-access graph database that aggregates vulnerability data from multiple sources and uses LLM embeddings to enable more accurate risk assessment and threat prioritization.