arXiv Machine Learning June 10, 2026 By Tatsuya Sakagami, Masashi Hisai, Naoto Yanai

Do LLMsMakeNeural Distinguishers Wise?

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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.

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llms

arXiv Machine Learning

Sep 16

Do LLMs Make Neural Distinguishers Wise?

The paper investigates whether large language models (LLMs) can enhance neural distinguishers, a cryptanalysis technique that uses machine learning to recover secret keys from plaintext–ciphertext pairs. Experiments on SPECK-32/64 show that LLM-based distinguishers do not outperform traditional ResNet models, that difference choice loses effectiveness at higher rounds, and that incorporating XOR operation results into the prompt significantly boosts LLM performance.

By Tatsuya Sakagami, Masashi Hisai, Naoto Yanai

llms

arXiv Machine Learning

Aug 26

Is the Hard-Label Cryptanalytic Model Extraction Really Polynomial?

arXiv:2510.06692v3 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have attracted significant attention, and their internal models are now considered valuable intellectual assets. Extrac...

By Akira Ito, Takayuki Miura, Yosuke Todo

arXiv Machine Learning

Sep 21

End-to-End Hard-Label Cryptanalytic Model Extraction Using Efficient Sign Recovery

arXiv:2609.21941v1 Announce Type: cross Abstract: The importance of deep neural networks (DNNs) is widely recognized, and the parameters obtained through training are regarded as valuable assets. Rec...

By Akira Ito, Takayuki Miura, Yosuke Todo

arXiv Machine Learning

Sep 10

Backdoor Channels Hidden in Latent Space: Extending Cryptographic Undetectability to Modern Neural Networks

arXiv:2605.13214v3 Announce Type: replace-cross Abstract: Recent cryptographic results establish that neural networks can be backdoored such that no efficient algorithm can distinguish them from a cl...

By Marte Eggen, Eirik Reiestad, Kristian Gj{\o}steen, Inga Str\"umke

llms diffusion computer-vision benchmarks

arXiv AI

Jun 24

Cycle-Consistent Neural Explanation of Formal Verification Certificates

arXiv:2606. 24414v1 Announce Type: new Abstract: Formal verification produces machine-checkable certificates that attest to the satisfaction or violation of temporal properties, yet these certificates remain opaque to non-specialist stakeholders.

By Andoni Rodriguez, Alberto Pozanco, Daniel Borrajo

llms

arXiv AI

Jul 28

Evaluating Large Language Models for Symbolic Security Protocol Analysis

arXiv:2607. 20712v1 Announce Type: cross Abstract: Security protocol verification relies on formal tools such as ProVerif and OFMC.

By Paolo Modesti, Syed Ahmed, Ioannis Sfyrakis, Derek Enodolomwanyi

llms benchmarks

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Do LLMs Make Neural Distinguishers Wise?

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