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arXiv · 2308.09490

Balancing Transparency and Risk: The Security and Privacy Risks of Open-Source Machine Learning Models

Dominik Hintersdorf·Lukas Struppek·Kristian Kersting
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Abstract

The field of artificial intelligence (AI) has experienced remarkable progress in recent years, driven by the widespread adoption of open-source machine learning models in both research and industry. Considering the resource-intensive nature of training on vast datasets, many applications opt for models that have already been trained. Hence, a small number of key players undertake the responsibility of training and publicly releasing large pre-trained models, providing a crucial foundation for a wide range of applications. However, the adoption of these open-source models carries inherent privacy and security risks that are often overlooked. To provide a concrete example, an inconspicuous model may conceal hidden functionalities that, when triggered by specific input patterns, can manipulate the behavior of the system, such as instructing self-driving cars to ignore the presence of other vehicles. The implications of successful privacy and security attacks encompass a broad spectrum, ranging from relatively minor damage like service interruptions to highly alarming scenarios, including physical harm or the exposure of sensitive user data. In this work, we present a comprehensive overview of common privacy and security threats associated with the use of open-source models. By raising awareness of these dangers, we strive to promote the responsible and secure use of AI systems.

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BibTeXRIS

Dominik Hintersdorf, Lukas Struppek, Kristian Kersting. 2023-08-18. Balancing Transparency and Risk: The Security and Privacy Risks of Open-Source Machine Learning Models. https://arxiv.org/abs/2308.09490

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