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Original Article

An explainable sbom-driven framework for software supply chain risk assessment and vulnerability prioritization

  • Paper ID 405
  • Volume 6, Issue 5
  • Pages 214-217
  • Published Online: September-October 2026
Open Access Peer Reviewed Crossref DOI

Authors

  • Anuj Goel1
  • Vaibhav Kumar2

1 M.Tech Student, Department of Computer Science Engineering & Application, Bhagwnat Institute of Technology, Muzaffarnagar, Uttar Pradesh, India. 2 Assistant Professors, Department of Computer Science Engineering & Application, Bhagwnat Institute of Technology, Muzaffarnagar, Uttar Pradesh, India.

Article Information

Published Online
September-October 2026
Pages
214-217
Article Type
Original Article
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Abstract

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Modern software applications depend on open-source libraries, transitive dependencies, build tools, and third-party components. Although software reuse improves development efficiency, it creates supply-chain risks because vulnerabilities in one component can affect numerous downstream applications. A Software Bill of Materials (SBOM) provides a machine-readable inventory of software components, yet an SBOM alone does not guarantee inventory completeness, correct component identification, or effective vulnerability prioritization. This paper proposes an explainable SBOM-driven framework combining inventory-quality checks, component normalization, public vulnerability intelligence, and transparent risk ranking. SBOMs will be generated for selected open-source projects and compared with available manifests or lockfiles. Component records will be enriched using CVE/NVD and OSV data, CVSS severity, EPSS exploitation probability, CISA Known Exploited Vulnerabilities (KEV) status, and dependency context. The proposed score will be compared with CVSS-only, EPSS-only, and KEV-first baselines. Since experiments have not yet been completed, this paper presents a research design rather than measured performance claims. The intended contribution is a reproducible methodology that makes ranking evidence, missing metadata, and uncertainty visible

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  • CrossRef DOI
  • ISSN 2583-1240
  • Open Access
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