Guess My Weight: Profiled Side-Channel Recovery of Floating-Point Neural-Network Weights
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-exact recovery of an IEEE-754 single-precision neural-network weight from power measurements. The attack targets the floating-point multiplication between a known input and a first-layer weight. During profiling, multivariate Gaussian templates are l...
| Authors | Timon Lumír Fillo, Ján Mikulec, Anubhab Baksi et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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