cs.CR · 2026-08-17 · No. 87

Cryptography and Security, 2026-08-17.

5 new papers in cs.CR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

5 entries
  1. 01

    A Hybrid LLM-Based Framework for Automated Security Annotation Generation in Business Process Models

    Md Kamrul Islam, Tiphaine Henry, Mattia Salnitri, Julius Köpke, Sami Souihi

    cs.CR · cs.AI · cs.SE

    The modelling and analysis of secure business processes require the incorporation of security annotations into process models. Although BPMN extensions, including SecBPMN2, exist for this purpose, the derivation of accurate and complete security annotations from natural-language specifications remains a manual, expert-intensive, and error-prone task. This paper presents a hybrid framework that takes a BPMN process model and a security...

    arxiv.org/abs/2608.14370 · PDF

  2. 02

    A Four-Axis Trustworthiness Benchmark for LLM-as-Judge in Principle-Based Regulation

    Dipankar Sarkar

    cs.CR · cs.AI · cs.CL · cs.CY · cs.LG

    Principle-based regulation, with evaluative standards such as "fair, clear, and not misleading" or "deliver good outcomes", cannot be reduced to binary predicates, and LLM-as-judge is increasingly used as the substitute. Our position is that any such judge must be evaluated on four axes: accuracy, paraphrase robustness, adversarial robustness, and calibration. We release Principle-Bench, 168 cryptoasset financial-promotion scenarios mapped to...

    arxiv.org/abs/2608.14329 · PDF

  3. 03

    BGA: A noise-immune neural distillation framework for malicious signature extraction in high-entropy encrypted flows

    Sheng Hong, Yixuan Huang, Weiwei Jiang, Junyuan Zhang, Jiacheng Wang, Ruijian Jiao

    cs.CR · cs.AI

    To mitigate attention dilution in high-entropy TLS 1.3 flows, we propose BGA, a noise-immune neural distillation framework for encrypted threat intelligence.The methodology first employs Analysis of Variance (ANOVA) to decouple high-discriminatory control-plane features - specifically industrial setpoints - from stochastic cryptographic noise. To resolve the extreme class imbalance within a corpus of 86,878 flow records, a Wasserstein GAN...

    arxiv.org/abs/2608.14126 · PDF

  4. 04

    P2Skill: Privacy Preserving Skill Distillation for Cloud-Local LLM Inference Systems

    Myunghoon Ryu, Geunpyo Park, Sungjoon Lee, XinYu Piao, Jong-Kook Kim

    cs.CR · cs.AI

    Cloud-local LLM inference systems have the potential to use the reasoning capability of large cloud models while protecting sensitive user data on personal devices. Cloud-bound requests must exclude personally identifiable information (PII) to prevent external data leakage. Existing privacy-preserving methods rely on prompt perturbation, entity masking, or model fine-tuning, but these approaches may distort contextual semantics or require...

    arxiv.org/abs/2608.14094 · PDF

  5. 05

    CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts

    Runhan Song, Qiqi Liu, Chuanzhou Pan, Zhenquan Ding, Youquan Xian, Chongru Fan, Lei Cui, Wei Wang, Zhiyu Hao

    cs.CR · cs.AI · cs.NI

    HTTPS website fingerprinting (WF) aims to identify visited websites from metadata observable in encrypted traffic. However, real-world deployments introduce a significant out-of-distribution (OOD) problem caused by temporal and geographic changes, while previously unseen websites are common in open-world scenarios. Existing methods primarily learn from raw TCP packet sequences and struggle to capture stable and generalizable website...

    arxiv.org/abs/2608.13905 · PDF

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