AmpereBleed: Exploiting On-chip Current Sensors for Circuit-Free Attacks on ARM-FPGA SoCs
Xin Zhang, Yi Yang, Jiajun Zou, Qingni Shen, Zhi Zhang, Yansong Gao, Zhonghai Wu, Trevor E. Carlson · 2025
FPGAs offer superior energy efficiency and performance in parallel computing but are vulnerable to remote power side-channel attacks. Existing attacks rely on assumptions of coresident crafted circuits and shared power delivery networks, limiting their practicality in real-world scenarios. In this paper, we present AmpereBleed, a novel current-based side-channel attack that exploits widely available INA226 sensors in ARMFPGA SoCs, bypassing the aforementioned two assumptions. AmpereBleed achieves $261 \times$ greater variations to victim activities compared to the popular ring oscillator (RO) circuit, fingerprints DNN models on the Xilinx Deep Learning Processor Unit (DPU) with $\mathbf{9 9. 7 \%}$ accuracy, and distinguishes the Hamming weights of RSA-1024 keys.