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Main Authors: Guyomard, Victor, Mauvisseau, Mathis, Paindavoine, Marie
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2509.06371
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author Guyomard, Victor
Mauvisseau, Mathis
Paindavoine, Marie
author_facet Guyomard, Victor
Mauvisseau, Mathis
Paindavoine, Marie
contents Due to hardware and software improvements, an increasing number of AI models are deployed on-device. This shift enhances privacy and reduces latency, but also introduces security risks distinct from traditional software. In this article, we examine these risks through the real-world case study of SafetyCore, an Android system service incorporating sensitive image content detection. We demonstrate how the on-device AI model can be extracted and manipulated to bypass detection, effectively rendering the protection ineffective. Our analysis exposes vulnerabilities of on-device AI models and provides a practical demonstration of how adversaries can exploit them.
format Preprint
id arxiv_https___arxiv_org_abs_2509_06371
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Breaking SafetyCore: Exploring the Risks of On-Device AI Deployment
Guyomard, Victor
Mauvisseau, Mathis
Paindavoine, Marie
Machine Learning
Due to hardware and software improvements, an increasing number of AI models are deployed on-device. This shift enhances privacy and reduces latency, but also introduces security risks distinct from traditional software. In this article, we examine these risks through the real-world case study of SafetyCore, an Android system service incorporating sensitive image content detection. We demonstrate how the on-device AI model can be extracted and manipulated to bypass detection, effectively rendering the protection ineffective. Our analysis exposes vulnerabilities of on-device AI models and provides a practical demonstration of how adversaries can exploit them.
title Breaking SafetyCore: Exploring the Risks of On-Device AI Deployment
topic Machine Learning
url https://arxiv.org/abs/2509.06371