Model Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices

Neural networks NNs are increasingly deployed on edge devices such as mobile and IoT platforms, offering low latency and improved privacy compared to cloud-based deployment. However, on-device models embedded in mobile applications can be unpacked and exposed to adversaries, enabling cross-node...

Mind Your Boundaries: Boundary-Aware Model Obfuscation against Cross-Node Attack

With the advancement of powerful AI chips, on-device machine learning is gaining traction for its low latency and privacy protection. However, when edge devices are compromised, attackers can obtain white-box access to the deployed models by reversing the apps directly to obtain the knowledge of...

Awesome-MoAI-Security — Updated!

Awesome Mobile On-Device AI Security SoK: Attack and Defense Landscape of Mobile On-device AI Systems Yujin Huang, Xin Zheng, Xingliang Yuan, Kwok-Yan Lam Mobile on-device AI systems execute AI models locally through ML frameworks such as LiteRT/TFLite , Core ML , ExecuTorch , ONNX , and...