Paper 2026/1811

Efficient Soft Analytical Side-Channel Attacks on Large-Scale Cryptographic Computations

Abstract

Soft Analytical Side-Channel Attacks (SASCA) combine leakage-derived priors from multiple intermediate variables with their functional dependencies through belief propagation (BP).However, when applying SASCA to large-scale cryptographic computations where algorithms are abstracted into extensive factor graphs with large candidate sets per variable node, the memory and computational complexity of SASCA become prohibitive. A natural first choice for large-domain variables is to fragment them into smaller-domain variables when the underlying computation decomposes accordingly. For modular addition and multiplication, however, preserving cross-fragment dependencies can introduce short cycles and coupled factor updates, motivating alternative inference strategies. We consider the Number Theoretic Transform (NTT) in ML-DSA as a representative large-scale cryptographic computation, where standard SASCA (with FFT optimization) requires approximately 122~GB of memory for message propagation in an unprotected single-trace setting, even for a 6-layer sub-NTT component, while masking further amplifies the graph size and inference cost. \vspace{0.3em} To address this limitation, we propose Greedy Region-Wise Pruning SASCA (GRWP-SASCA), a practical and efficient framework that enables scalable inference by dividing the global factor graph into manageable regions. The core idea is to replace global BP with a sequence of localized inference steps, where regions are incrementally merged, and the search space is reduced via greedy pruning of redundant structures and low-confidence candidates, thereby significantly reducing the memory and computational complexity of the BP algorithm. This design provides a flexible attack strategy for large-domain arithmetic factor graphs. Our results show that GRWP-SASCA transforms previously infeasible SASCA attacks on large-scale cryptographic computations into practical ones. Theoretical analysis of single-trace attacks on unprotected ML-DSA shows that GRWP-SASCA reduces the memory overhead associated with message propagation by a factor of up to $151$ compared to the standard SASCA, while achieving an estimated speedup by a factor of $68$. For $d$-order masked ML-DSA with multiple (say, $t$) traces, GRWP-SASCA achieves an estimated reduction in memory overhead associated with message propagation by a factor of up to $388.13t(d+1.14)/(d+2.44)$, while achieving a speedup by a factor of approximately $2d + 4$. Real-device experiments on an ARM Cortex-M4 platform demonstrate that the secret can be recovered from an unprotected implementation within approximately $15$ minutes with a single trace, with a peak message memory usage of approximately 0.9 GB. For first-order masked ML-DSA, the secret can be recovered within 3.2 hours using 8 traces, with a peak message memory usage of approximately 8.1 GB.