aiqbench analysis
This study finds that applying large language models to neural distinguishers for symmetric-key cryptography offers no performance advantage over traditional ResNet-based approaches. While the choice of plaintext differences proves ineffective at high rounds regardless of the model, incorporating XOR operation results into the prompt significantly enhances distinguisher performance. The key takeaway for security researchers is that LLMs do not inherently strengthen this specific cryptanalysis method, though prompt engineering remains a viable optimization lever.
Do LLMsMakeNeural Distinguishers Wise?
arXiv:2606.10692v1 Announce Type: cross Abstract: 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.
Title:Do LLMsMakeNeural Distinguishers Wise?
Abstract: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.
Cryptography and Security (cs.CR); Machine Learning (cs.LG)
arXiv:2606.10692 [cs.CR]
(or arXiv:2606.10692v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.10692 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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