Do LLMs Make Neural Distinguishers Wise?
Neural distinguishers are a cryptanalysis method for symmetric-key cryptography that trains machine learning models on pairs of plaintexts and ciphertexts with specific differences in order to recover a secret key. To the best of our knowledge, no existing work has explored the use of large language models (LLMs) for neural distinguishers. In this paper, we propose LLM-based neural distinguishers through a prompt design and conduct extensive experiments with them on SPECK-32/64 to investigate whether LLMs can strengthen neural distinguishers. We then found three key insights. First, by comparing the results of LLM-based neural distinguishers with ResNet in the existing work, we demonstrat...
| Authors | Tatsuya Sakagami, Masashi Hisai, Naoto Yanai |
|---|---|
| Primary category | cs.CR |
| Published | 9 June 2026 |
| Last updated at source | 15 September 2026 |
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