Transactions on Symmetric Cryptology, Volume 2025
AutoDiVer: Automatically Verifying Differential Characteristics and Learning Key Conditions
Marcel Nageler
Graz University of Technology, Austria
Shibam Ghosh
Inria, Paris, France
Marlene Jüttler
Graz University of Technology, Austria
Maria Eichlseder
Graz University of Technology, Austria
Keywords: Differential cryptanalysis, Hypothesis of stochastic equivalence, Tool, SAT solver, GIFT, SKINNY, Midori, WARP, SPECK, SPEEDY
Abstract
Differential cryptanalysis is one of the main methods of cryptanalysis and has been applied to a wide range of ciphers. While it is very successful, it also relies on certain assumptions that do not necessarily hold in practice. One of these is the hypothesis of stochastic equivalence, which states that the probability of a differential characteristic behaves similarly for all keys. Several works have demonstrated examples where this hypothesis is violated, impacting the attack complexity and sometimes even invalidating the investigated prior attacks. Nevertheless, the hypothesis is still typically taken for granted. In this work, we propose AutoDiVer, an automatic tool that allows to thoroughly verify differential characteristics. First, the tool supports calculating the expected probability of differential characteristics while considering the key schedule of the cipher. Second, the tool supports estimating the size of the space of keys for which the characteristic permits valid pairs, and deducing conditions for these keys. AutoDiVer implements a custom SAT modeling approach and takes advantage of a combination of features of advanced SAT solvers, including approximate model counting and clause learning. To show applicability to many different kinds of block ciphers like strongly aligned, weakly aligned, and ARX ciphers, we apply AutoDiVer to GIFT, PRESENT, RECTANGLE, SKINNY, WARP, SPECK, and SPEEDY.
Publication
Transactions on Symmetric Cryptology, Volume 2025, Issue 1
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fse/2025/a6
Artifact published
September 18, 2025
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License
This work is licensed under the MIT License.
Note that license information is supplied by the authors and has not been confirmed by the IACR.
BibTeX How to cite
Nageler, M., Ghosh, S., Jüttler, M., & Eichlseder, M. (2025). AutoDiVer: Automatically Verifying Differential Characteristics and Learning Key Conditions. IACR Transactions on Symmetric Cryptology, 2025(1), 471-514. https://doi.org/10.46586/tosc.v2025.i1.471-514. Artifact available at https://artifacts.iacr.org/fse/2025/a6