研究手机端AI安全风险，揭示模型可被提取篡改
Breaking SafetyCore: Exploring the Risks of On-Device AI Deployment
- 通过SafetyCore案例分析手机端AI漏洞
- 实证攻击可绕过图像内容检测机制
- 警示开发者注意设备端AI防护缺陷
随着软硬件进步，越来越多AI模型被部署在设备端，提升了隐私保护和降低了延迟，但也引入了与传统软件不同的安全风险。本文通过SafetyCore这一Android系统服务的真实案例，研究其内置敏感图像内容检测功能的安全部署。我们证明了攻击者可成功提取并操纵该设备端AI模型，从而绕过检测机制，使防护失效。分析揭示了设备端AI模型存在的安全隐患，并提供了实际攻击演示，为后续安全设计提供重要参考。
原文摘要 · Abstract (English)
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.
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