HyperTheft: Thieving Model Weights from TEE-Shielded Neural Networks via Ciphertext Side Channels
Yuanyuan Yuan, Zhibo Liu, Sen Deng, Yanzuo Chen, Shuai Wang, Yinqian Zhang, Zhendong Su · 2024
Trusted execution environments (TEEs) are widely employed to protect deep neural networks (DNNs) from untrusted hosts (e.g., hypervisors). By shielding DNNs as fully black-box via encryption, TEEs mitigate model weight leakage and its follow-up white-box attacks. However, this paper uncovers that the confidentiality of TEE-shielded DNNs can be violated due to an emerging threat towards TEEs: ciphertext side channels of TEEs create weight-dependent observations during a DNN's execution. Despite the potential of inferring DNN weights from ciphertext side channels, existing techniques are inapplicable due to their over-strong requirements and the high precision required by DNN weights. A DNN can have millions of weight elements, and even a few incorrectly recovered weight elements may make the DNN non-functional.