8 citations · 17 across the 9 of their papers we have counts for
5 papers · 1 filter
Detecting and Understanding Vulnerabilities in Fully Homomorphic Encryption Frameworks
Yiteng Peng, Dongwei Xiao, Zhibo Liu +2
Fully homomorphic encryption (FHE) allows computations to be performed directly on encrypted data without decryption, offering strong privacy guarantees for sensitive data analysis…
Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces in DNN Executables
Yanzuo Chen, Zhibo Liu, Yuanyuan Yuan +3
Bit-flip attacks (BFAs) can manipulate deep neural networks (DNNs). For high-level DNN models running on deep learning (DL) frameworks like PyTorch, extensive BFAs have been used t…
Cache Refinement Type for Side-Channel Detection of Cryptographic Software
Ke Jiang, Yuyan Bao, Shuai Wang +2
Cache side-channel attacks exhibit severe threats to software security and privacy, especially for cryptosystems. In this paper, we propose CaType, a novel refinement type-based to…
Decompiling x86 Deep Neural Network Executables
Zhibo Liu, Yuanyuan Yuan, Shuai Wang +2
Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitiv…
CacheQL: Quantifying and Localizing Cache Side-Channel Vulnerabilities in Production Software
Yuanyuan Yuan, Zhibo Liu, Shuai Wang
Cache side-channel attacks extract secrets by examining how victim software accesses cache. To date, practical attacks on cryptosystems and media libraries are demonstrated under d…