Profiled Deep-Learning Side-Channel Analysis of the MAYO Secret Linear Map on a Cortex-M4: Exact Key-Rank Assessment, Leakage Mechanism, and a Measured First-Order Masking Countermeasure

Post-quantum migration moves the security of digital signatures onto their embedded implementations. This work presents a profiled deep-learning side-channel analysis of the secret linear map of MAYO, a multivariate Unbalanced Oil and Vinegar signature in the third round of the NIST additional-signatures process, and a hardware-validated first-order masking countermeasure. A reduced target isolating the secret product Ox on an STM32F303 (Cortex-M4) is assessed under a single budget of 50,000 profiling and 10,000 attack traces. Attacker effort is reported as the exact key rank of one oil vector over the 2312 key space; the usual product-of-ranks proxy understates it by 37 to 80 bits. The best oil vector of a genuine MAYO-1 key is recovered to an exact rank near 261, with 58 of 78 nibbles exact, and the residual uncertainty collapses into a two-dimensional GF(2) coset that a per-bit leakage map explains. Over its whole operation, the proposed masked build keeps profiled and bivariate second-order attacks at random guessing. Leakage assessment nonetheless localises Hamming-weight leaks of eleven nibbles to a memory-bus adjacency and to unmasked key handling, both confirmed with firmware variants. A corrected build shows no detectable first-order leakage of any key nibble, at 2.14× cycles and 1.32× memory.