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.
@article{arxiv.2509.06371,
title = {Breaking SafetyCore: Exploring the Risks of On-Device AI Deployment},
author = {Victor Guyomard and Mathis Mauvisseau and Marie Paindavoine},
journal= {arXiv preprint arXiv:2509.06371},
year = {2025}
}