Guess My Weight: Profiled Side-Channel Recovery of Floating-Point Neural-Network Weights

Excerpt

arXiv:2610.04436v1 Announce Type: cross Abstract: Neural-network parameters deployed on embedded devices may be exposed through physical side-channel leakage during inference. Existing side-channel attacks on floating-point neural-network parameters have often targeted reduced numerical precision, while recovering the complete IEEE-754 representation remains considerably more challenging because of the large and structured 32-bit candidate space. We present a profiled template attack for bit-exa