Measuring the Security of the Evolving Software Supply Chain: a Research Agenda
Researchers propose a unified cross-ecosystem measurement agenda for software supply chain security, targeting dependency modeling and AI-generated dependency patterns.
The paper argues that existing quantitative measurement and vulnerability management approaches for software supply chain security are fragmented and ecosystem-specific, limiting comparable risk assessments. It lays out a research agenda starting with a Systematization of Knowledge to expose gaps in dependency modeling, transitive dependency treatment, and real-world exploitability of vulnerabilities. It further warns that AI-assisted development with coding LLMs will create dependency patterns not captured by traditional Software Composition Analysis tools, motivating a rethink of dependency modeling.
- Current supply chain risk measurement is fragmented per ecosystem
- SoK planned to expose dependency modeling and propagation gaps
- Transitive dependency exploitability is under-analyzed
- Coding LLMs may create dependency patterns invisible to SCA tools
Full article170 words · extracted from arxiv.org · click to collapse
Software supply chain security has become increasingly critical due to the widespread reliance on third-party dependencies and the growing attack surface of modern software ecosystems. However, existing quantitative, measurement-based analysis and vulnerability management approaches remain largely fragmented and ecosystem-specific, limiting their ability to provide comparable risk assessments across environments. This paper presents a structured research plan, starting with a Systematization of Knowledge (SoK) to synthesize the current state of research and identify key gaps, highlighting the limitations in dependency modeling and vulnerability propagation analysis, particularly in the treatment of transitive dependencies and their real-world exploitability. Based on these insights, we argue for a unified measurement perspective capable of consistently representing and analyzing the cross-ecosystem dependency structure. We further identify emerging challenges introduced by AI-assisted software development, where coding LLMs are likely to contribute to new dependency patterns that are not captured by traditional Software Composition Analysis (SCA) tools. These shifts motivate a rethink of dependency modeling to account for evolving software-generation practices and their long-term structural impact on software security.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.08810