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ASE2022Top-tier venue

Insight: Exploring Cross-Ecosystem Vulnerability Impacts

Meiqiu Xu, Ying Wang, Shing-Chi Cheung, Hai Yu, Zhiliang Zhu

2022Year
12Citations
2Top-tier citations

Abstract

Vulnerabilities, referred to as CLV issues, are induced by cross-language invocations of vulnerable libraries. Such issues greatly increase the attack surface of Python/Java projects due to their pervasive use of C libraries. Existing Python/Java build tools in PyPI and Maven ecosystems fail to report the dependency on vulnerable libraries written in other languages such as C. CLV issues are easily missed by developers. In this paper, we conduct the first empirical study on the status quo of CLV issues in PyPI and Maven ecosystems. It is found that 82,951 projects in these ecosystems are directly or indirectly dependent on libraries compiled from the C project versions that are identified to be vulnerable in CVE reports. Our study arouses the awareness of CLV issues in popular ecosystems and presents related analysis results.

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