aiqbench analysis
This study investigates whether Large Language Models can enhance neural distinguishers, a cryptanalysis technique used in symmetric-key cryptography, finding no performance gain over traditional ResNet architectures. The research highlights that while LLMs do not inherently strengthen these models, their utility improves significantly when prompts are restricted to XOR operation results rather than raw data differences. Ultimately, the findings suggest that integrating generative AI into low-level cryptographic attacks offers limited advantage without specific architectural adjustments.
Do LLMs Make Neural Distinguishers Wise?
arXiv:2606.10692v2 Announce Type: replace-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 LLMs Make Neural 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:2606.10692v2 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.10692 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 264 words
Related coverage
Presentation: From Thousands to One: Building LLM-Powered Selection Systems
InfoQ · Aug 17, 2026
Model Weight Profiles: Where Do the Parameters Go?
AMD · Sep 25, 2026
What's Continual Learning, and Why Might We Expect To See It In Advanced LLM Agents?
LessWrong · Jun 12, 2026
Do LLMsMakeNeural Distinguishers Wise?
arXiv · Jun 10, 2026