On the Effect of Transitivity and Granularity on Vulnerability Propagation in the Maven Ecosystem
Not on the map. Why?
Abstract (arXiv)
Reusing software libraries is a pillar of modern software engineering. In 2022, the average Java application depends on 40 third-party libraries. Relying on such libraries exposes a project to potential vulnerabilities and may put an application and its users at risk. Unfortunately, research on software ecosystems has shown that the number of projects that are affected by such vulnerabilities is rising. Previous investigations usually reason about dependencies on the dependency level, but we believe that this highly inflates the actual number of affected projects. In this work, we study the effect of transitivity and granularity on vulnerability propagation in the Maven ecosystem. In our research methodology, we gather a large dataset of 3M recent Maven packages. We obtain the full transitive set of dependencies for this dataset, construct whole-program call graphs, and perform reachability analysis. This approach allows us to identify Maven packages that are actually affected by using vulnerable dependencies. Our empirical results show that: (1) about 1/3 of packages in our dataset are identified as vulnerable if and only if all the transitive dependencies are considered. (2) less than 1% of packages have a reachable call path to vulnerable code in their dependencies, which is far lower than that of a naive dependency-based analysis. (3) limiting the depth of the resolved dependency tree might be a useful technique to reduce computation time for expensive fine-grained (vulnerability) analysis. We discuss the implications of our work and provide actionable insights for researchers and practitioners.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jan 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2026Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Reviewcited by 3
- 2024On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Studycited by 1
- 2025Which Is Better For Reducing Outdated and Vulnerable Dependencies: Pinning or Floating?cited by 0
- 2026From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practicecited by 0
- 2026From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practice18 Jul 2026
- 2026Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review19 Mar 2026
- 2025Which Is Better For Reducing Outdated and Vulnerable Dependencies: Pinning or Floating?7 Oct 2025
- 2024On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study11 Feb 2024