  As Field-programmable gate arrays (FPGAs) are widely adopted in clouds to
accelerate Deep Neural Networks (DNN), such virtualization environments have
posed many new security issues. This work investigates the integrity of DNN
FPGA accelerators in clouds. It proposes DeepStrike, a remotely-guided attack
based on power glitching fault injections targeting DNN execution. We
characterize the vulnerabilities of different DNN layers against fault
injections on FPGAs and leverage time-to-digital converter (TDC) sensors to
precisely control the timing of fault injections. Experimental results show
that our proposed attack can successfully disrupt the FPGA DSP kernel and
misclassify the target victim DNN application.
