Research paper proposing machine learning methods to predict multi-vulnerability attack chains in software supply chains using Software Bill of Materials (SBOM) graphs. Combines graph analysis with ML to identify sequences of exploitable vulnerabilities and cascading attack vectors. Addresses critical security challenge in modern software dependency ecosystems.
Research
Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs
ML model predicts multi-step vulnerability attack chains in software supply chains by analyzing Software Bill of Materials graphs—automating detection of cascading exploit sequences across dependencies.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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