cs.CR · 2026-07-02 · No. 41

Cryptography and Security, 2026-07-02.

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

    All-out Attack: Optimal Block Withholding Under Pay-Per-Share Scheme

    Mustafa Doger, Sennur Ulukus

    cs.CR · cs.DC · cs.IT · math.PR

    Classical Block Withholding (BWH) attacks have been extensively studied in block-dependent reward schemes, where pool members are compensated upon a block discovery within the pool. However, most contemporary mining pools operate under share-based scheme wherein participants are paid immediately upon submission of valid shares. In this paper, we analyze BWH under Pay-Per-Share (PPS) and Full-PPS (FPPS) schemes for Nakamoto-style blockchains...

    arxiv.org/abs/2607.01209 · PDF

  2. 02

    Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence

    Jose Luis Vela Alonso, Carmen Pellicer

    cs.CR · cs.LG

    Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat original evidence as training data and produce opaque classifications without instance-level justification. This paper presents a forensic-oriented intrusion detection framework resolving both problems simultaneously,...

    arxiv.org/abs/2607.00763 · PDF

  3. 03

    Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

    MD Azizul Hakim, Md Shihab Uddin, Talha Ibne Anis

    cs.CR · cs.AI

    Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment. Most reported results evaluate these models only within their training network, leaving behavior on unseen networks unverified. This study trains four lightweight architectures on one IIoT dataset and evaluates them, without retraining, on two...

    arxiv.org/abs/2607.00553 · PDF

  4. 04

    Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces

    Junlong Liu, Haobo Wang, Weiqi Luo, Xiaojun Jia

    cs.CR · cs.AI

    Jailbreak attacks remain a critical threat to the safe deployment of large language models (LLMs). While prior work has primarily studied attacks and defenses at the prompt level, we show that this prompt-centric paradigm overlooks a structural vulnerability in stateful, function-calling environments. In such applications, developer-defined schemas, structured arguments, and untrusted tool outputs are interleaved into a single shared model...

    arxiv.org/abs/2607.00481 · PDF

  5. 05

    SoK: Attack and Defense Landscape of Mobile On-device AI Systems

    Yujin Huang, Xin Zheng, Xingliang Yuan, Kwok-Yan Lam

    cs.CR · cs.AI · cs.LG

    Mobile on-device AI (MoAI) systems that integrate locally deployed AI models with conventional mobile software components are emerging as a key paradigm for delivering intelligent functionality directly on end-user devices. By moving inference from remote cloud services to the local mobile environment, such systems enable privacy-preserving, low-latency, and offline-capable AI functionality, yet introduce new security risks arising from the...

    arxiv.org/abs/2607.00362 · PDF

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