From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem

Abstract:Hidden license conflicts in the open-source AI ecosystem pose serious legal and ethical risks, exposing organizations to potential litigation and users to undisclosed risk. However, the field lacks a data-driven understanding of how frequently these conflicts occur, where they originate, and which communities are most affected. We present the first end-to-end audit of licenses for datasets and models on Hugging Face, as well as their downstream integration into open-source software applications, covering 364 thousand datasets, 1.6 million models, and 140 thousand GitHub projects. Our empirical analysis reveals systemic non-compliance in which 35.5% of model-to-application transitions eliminate restrictive license clauses by relicensing under permissive terms. In addition, we prototype an extensible rule engine that encodes almost 200 SPDX and model-specific clauses for detecting license conflicts, which can solve 86.4% of license conflicts in software applications. To support future research, we release our dataset and the prototype engine. Our study highlights license compliance as a critical governance challenge in open-source AI and provides both the data and tools necessary to enable automated, AI-aware compliance at scale.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2509.09873 [cs.SE]

(or arXiv:2509.09873v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2509.09873 Focus to learn more arXiv-issued DOI via DataCite

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