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 demonstrate that LLMs provide no observable improvement in the performance of neural distinguishers. Second, we confirm that, at high rounds, the choice of differences is no longer effective for LLM-based neural distinguishers as well as ResNet. Third, we show that the performance of LLM-based neural distinguishers can be significantly improved by incorporating only the XOR operation results as a prompt design.

翻译：神经区分器是一种针对对称密钥密码的密码分析方法，它通过训练机器学习模型处理具有特定差异的明文与密文对，以恢复秘密密钥。据我们所知，目前尚无研究探索将大语言模型用于神经区分器。本文通过提示设计提出基于大语言模型的神经区分器，并在SPECK-32/64上开展广泛实验，以探究大语言模型能否增强神经区分器。我们发现了三个关键结论：第一，通过将基于大语言模型的神经区分器与现有工作中的ResNet进行对比，证明大语言模型并未显著提升神经区分器的性能；第二，我们确认在较高轮数下，差异选择对基于大语言模型的神经区分器与ResNet均不再有效；第三，我们表明，仅在提示设计中加入异或操作结果，即可显著提升基于大语言模型的神经区分器性能。