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
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: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: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
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
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
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. We propose a cycle-consistent neural architecture that generates faithful natural language explanations of verification certificates.
arXiv:2608. 02995v1 Announce Type: cross Abstract: Modern large language models (LLMs) exhibit activation sparsity, wherein only a subset of their neurons is activated for given input tokens.
By Yongwan Jo, Jinyoung Park, Euihyun Lee, Dokyung Song
SpliTEE extends the split‑inference architecture of Slalom to large language models by protecting intermediate GPU computations with differential privacy rather than encryption. The authors show that masking intermediate representations is essential, as a prompt‑reconstruction attack can recover prompts with about 80% accuracy. Their global sensitivity analysis bounds the noise needed, and they demonstrate that SpliTEE on Intel TDX achieves near‑double the speed of fully CPU‑based inference and outperforms encryption‑based Slalom while maintaining higher accuracy.
By Shashie Dilhara Batan Arachchige, Robin Carpentier, Hassan Jameel Asghar, Dali Kaafar
arXiv:2609.13636v1 Announce Type: cross
Abstract: Privacy-preserving inference via Torus Fully Homomorphic Encryption (TFHE) provides strong protection for sensitive data in outsourced deep learning...
By Mahmoud Y. M. Yassin, Mahmoud AbdelHafeez Sayed, Mostafa Taha
arXiv:2506. 14003v5 Announce Type: replace Abstract: Machine unlearning (MU) for large language models (LLMs), commonly referred to as LLM unlearning, seeks to remove specific undesirable data or knowledge from a trained model, while maintaining its performance on standard tasks.
By Yiwei Chen, Soumyadeep Pal, Yimeng Zhang, Qing Qu, Sijia Liu
arXiv:2607. 02072v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts.
By Mahmoud Abdelfattah, Hamid Nasiri, Peter Garraghan