Title

An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain

Document Type

Conference Proceeding

Publication Date

11-11-2022

Publication Title

SCORED'22: Proceedings of the 2022 ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses

Pages

105-114

Publisher Name

Association for Computing Machinery

Abstract

Deep neural networks achieve state-of-the-art performance on many tasks, but require increasingly complex architectures and costly training procedures. Engineers can reduce costs by reusing a pre-trained model (PTM) and fine-tuning it for their own tasks. To facilitate software reuse, engineers collaborate around model hubs, collections of PTMs and datasets organized by problem domain. Although model hubs are now comparable in popularity and size to other software ecosystems, the associated PTM supply chain has not yet been examined from a software engineering perspective.

We present an empirical study of artifacts and security features in 8 model hubs. We indicate the potential threat models and show that the existing defenses are insufficient for ensuring the security of PTMs. We compare PTM and traditional supply chains, and propose directions for further measurements and tools to increase the reliability of the PTM supply chain.

Identifier

ISBN: 978-1-4503-9885-5

Comments

Author Posting © Association for Computing Machinery, 2022. This article is posted here by permission of the Association for Computing Machinery for personal use, not for redistribution. The article was published in SCORED'22: Proceedings of the 2022 ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses, Pages 105-114, November 2022. https://www.doi.org/10.1145/3560835.3564547

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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