Survey: Six Years of Neural Differential Cryptanalysis

At CRYPTO 2019, A. Gohr introduced Neural Differential Cryptanalysis and used deep learning to improve the state of the art in cryptanalysis of 11-round SPECK32. As of February 2025, Gohr’s article has been cited 238 times on Google Scholar. The variety of targeted cryptographic primitives, techniques, settings, and evaluation methodologies that appear in these follow-up works provides a basis for a careful survey, which we provide in this paper. More specifically, we propose a taxonomy of these 238 publications and systematically review the 71 papers focusing on neural differential distinguishers, pointing out promising directions and recent advances in explainability and key-recovery attacks. We then highlight future challenges in the field, particularly the need for improved comparability of neural distinguishers and advancements in scaling. This holistic survey helps researchers and engineers to identify the leading neural differential attacks, compare their performance, and highlight the outstanding open problems in AI-assisted cryptanalysis.