Paper 2026/785

Neural Leakage–based Cryptanalysis of LowMC with Linear Complexity

Abstract

MPC-in-the-Head protocols enable post-quantum digital signatures based solely on symmetric primitives, with PICNIC being a prominent example built on the LowMC block cipher. While existing analyses assume exact Boolean circuit semantics, recent advances in neural representations suggest that piecewise-linear implementations may introduce activation boundary leakage. In this work, we investigate whether such leakage can be exploited in the context of LowMC and MPC-in-the-Head transcripts. We propose a perturbation-based probing methodology that models neural leakage and reduces round-key recovery to independent binary hypothesis tests via majority voting. Exploiting the linear structure of the LowMC key schedule, we demonstrate that recovery of the first-round key enables efficient reconstruction of the master key with linear complexity. Experimental results confirm successful recovery of 128-, 192-, and 256-bit keys under the proposed model, highlighting a new dimension in symmetric cryptanalysis and the need to consider learning-based leakage in future designs.