Paper 2025/396

Trail-Estimator: An Automated Verifier for Differential Trails in Block Ciphers

Abstract

Differential cryptanalysis is a powerful technique for attacking block ciphers, wherein the Markov cipher assumption and stochastic hypothesis are commonly employed to simplify the search and probability estimation of differential trails. However, these assumptions often neglect inherent algebraic constraints, potentially resulting in invalid trails and inaccurate probability estimates. Some studies identified violations of these assumptions and explored how they impose constraints on key material, but they have not yet fully captured all relevant ones. This study proposes Trail-Estimator, an automated verifier for differential trails on block ciphers, consisting of two parts: a constraint detector Cons-Collector and a solving tool Cons-Solver. We first establish the fundamental principles that will allow us to systematically identify all constraint subsets within a differential trail, upon which Cons-Collector is built. Then, Cons-Solver utilizes specialized preprocessing techniques to efficiently solve the detected constraint subsets, thereby determining the key space and providing a comprehensive probability distribution of differential trails. To validate its effectiveness, Trail-Estimator is applied to verify 14 differential trails for the SKINNY, LBLOCK, and TWINE block ciphers. Experimental results show that Trail-Estimator consistently identifies previously undetected constraints for SKINNY and discovers constraints for the first time for LBLOCK and TWINE. Notably, it is the first tool to discover long nonlinear constraints extending beyond five rounds in these ciphers. Furthermore, Trail-Estimator's accuracy is validated by experiments showing its predictions closely match the real probability distribution of short-round differential trails.