用大模型做密码分析效果不佳，但加异或结果能提升性能
Do LLMsMakeNeural Distinguishers Wise?
- 用提示词设计让大模型处理密文对，尝试替代传统神经网络
- 大模型在高轮次下无法识别差异，性能不如残差网络
- 仅加入异或结果作提示，显著提升大模型辨识能力
神经区分器是针对对称密码的一种密码分析方法，通过在特定差异的明文-密文对上训练机器学习模型来恢复密钥。据我们所知，尚无工作探索使用大语言模型（LLMs）构建神经区分器。本文提出基于大语言模型的神经区分器，通过提示词设计，并在SPECK-32/64上进行大量实验，探究大模型能否增强神经区分器性能。我们发现三个关键结论：第一，与现有研究中的ResNet相比，大模型未带来可观察的性能提升；第二，高轮次下，差异选择对大模型和ResNet均不再有效；第三，仅通过将异或运算结果作为提示输入，即可显著提升大模型区分器的性能。
原文摘要 · Abstract (English)
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.
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