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Open-Source Dependency Vulnerability Drift Monitor

Author

Listed:
  • Devadath SR

    (Dept. of Computer Science & Engineering, Federal Institute of Science and Technology Ernakulam)

  • Dhipa Mariya Paul.

    (Dept. of Computer Science & Engineering, Federal Institute of Science and Technology Ernakulam)

  • Ganga MU.

    (Dept. of Computer Science & Engineering, Federal Institute of Science and Technology Ernakulam)

  • Harinanda J.

    (Dept. of Computer Science & Engineering, Federal Institute of Science and Technology Ernakulam)

  • Ms.Nithya Paul

    (Department of Computer Science and Engineering, Federal Institute of Science and Technology Ernakulam)

Abstract

This paper presents an Open-Source Dependency Vulnerability Drift Monitor , an automated security monitoring platform designed to continuously track software dependencies for known vulnerabilities and version inconsistencies. Most existing vulnerability scanning tools provide only one-time analysis and fail to address the continuous evolution of dependencies, leaving systems exposed to newly discovered threats. For development teams, the system provides automated dependency scanning, real-time vulnerability detection using the OSV database, version drift analysis, and prioritized security alerts. For individual developers, the platform offers a centralized dashboard for monitoring multiple projects with detailed CVE information and remediation guidance. The system includes features such as automated dependency extraction, vulnerability detection, version drift monitoring, risk-based alert generation, and scheduled background scanning. It was evaluated with real-world npm and Python projects, achieving over 90% accuracy in vulnerability detection and successfully identifying outdated dependencies across all tested scenarios

Suggested Citation

  • Devadath SR & Dhipa Mariya Paul. & Ganga MU. & Harinanda J. & Ms.Nithya Paul, 2026. "Open-Source Dependency Vulnerability Drift Monitor," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 11(6), pages 2501-2507, June.
    • Handle: RePEc:bjf:journl:v:11:y:2026:i:6:p:2501-2507
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