VDGraph: A Graph-Theoretic Approach to Unlock Insights from SBOM and SCA Data

• URL: http://arxiv.org/abs/2507.20502v1
• Date: Mon, 28 Jul 2025 03:43:29 GMT
• Title: VDGraph: A Graph-Theoretic Approach to Unlock Insights from SBOM and SCA Data
• Abstract summary: We introduce VDGraph, a knowledge graph-based methodology for integrating vulnerability and dependency data into a holistic view.<n>VDGraph consolidates Software Bill of Materials (SBOM) and Software Composition Analysis (SCA) outputs into a graph representation of software projects' dependencies and vulnerabilities.<n>We show that vulnerabilities predominantly emerge at a depth of three dependency levels or higher, indicating that direct or secondary dependencies exhibit lower vulnerability density and tend to be more secure.
• Score: 1.1417805445492082
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The high complexity of modern software supply chains necessitates tools such as Software Bill of Materials (SBOMs) to manage component dependencies, and Software Composition Analysis (SCA) tools to identify vulnerabilities. While there exists limited integration between SBOMs and SCA tools, a unified view of complex dependency-vulnerability relationships remains elusive. In this paper, we introduce VDGraph, a novel knowledge graph-based methodology for integrating vulnerability and dependency data into a holistic view. VDGraph consolidates SBOM and SCA outputs into a graph representation of software projects' dependencies and vulnerabilities. We provide a formal description and analysis of the theoretical properties of VDGraph and present solutions to manage possible conflicts between the SBOM and SCA data. We further introduce and evaluate a practical, proof-of-concept implementation of VDGraph using two popular SBOM and SCA tools, namely CycloneDX Maven plugin and Google's OSV-Scanner. We apply VDGraph on 21 popular Java projects. Through the formulation of appropriate queries on the graphs, we uncover the existence of concentrated risk points (i.e., vulnerable components of high severity reachable through numerous dependency paths). We further show that vulnerabilities predominantly emerge at a depth of three dependency levels or higher, indicating that direct or secondary dependencies exhibit lower vulnerability density and tend to be more secure. Thus, VDGraph contributes a graph-theoretic methodology that improves visibility into how vulnerabilities propagate through complex, transitive dependencies. Moreover, our implementation, which combines open SBOM and SCA standards with Neo4j, lays a foundation for scalable and automated analysis across real-world projects.

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