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Considering the resource-intensive nature of training\u00a0on vast datasets, many applications opt for models that have already been trained. Hence, a small number of key players undertake\u00a0the responsibility of training and publicly releasing large pre-trained models, providing a crucial foundation for a wide range\u00a0of applications. However, the adoption of these open-source models carries inherent privacy and security risks that are\u00a0often overlooked. To provide a concrete example, an inconspicuous\u00a0model may conceal hidden functionalities that, when triggered by specific input patterns, can manipulate the behavior of the system, such\u00a0as instructing self-driving cars to ignore the presence of\u00a0other vehicles. The implications of successful privacy and security attacks encompass a broad spectrum, ranging from relatively\u00a0minor damage like service interruptions to highly alarming scenarios, including physical harm or the exposure of sensitive user data.\u00a0In this work, we present a comprehensive overview of common privacy\u00a0and security threats associated with the use of open-source models.\u00a0By raising awareness of these dangers, we strive to promote\u00a0the responsible and secure use of AI systems.<\/jats:p>","DOI":"10.1007\/978-3-031-73741-1_16","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T14:54:58Z","timestamp":1730300098000},"page":"269-283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Balancing Transparency and Risk: An Overview of the Security and Privacy Risks of Open-Source Machine Learning Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4976-6894","authenticated-orcid":false,"given":"Dominik","family":"Hintersdorf","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0626-3672","authenticated-orcid":false,"given":"Lukas","family":"Struppek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2873-9152","authenticated-orcid":false,"given":"Kristian","family":"Kersting","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Bourtoule, L., et al.: Machine unlearning. 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