cs.CR · 2026-06-10 · No. 19
Cryptography and Security, 2026-06-10.
10 new papers in cs.CR. Titles, authors,
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01 — The papers
10 entries-
01
Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017
Zach Moczkodan, Hany Ragab
cs.CR · cs.LG
Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017. However, many existing studies neither supply their temporal modules with genuine sequence inputs nor evaluate under realistic, leakage-free conditions, making it unclear whether reported gains arise from true sequence-modeling...
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02
Understanding and mitigating the risks of OpenClaw for non-technical users: A practical guide with Skill
Junchang Zheng, Junfeng Tan, Jialiang Lin
cs.CR · cs.AI · cs.SE
OpenClaw has rapidly emerged as a transformative artificial intelligence (AI) agent framework, and its ability to autonomously execute complex, multi-step tasks has attracted an ever-growing and diverse user base. However, this capability comes with significant risks. While existing research has made important strides in characterizing these threats, such work is predominantly directed at technically sophisticated audiences. It remains...
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03
A Bayesian Network Approach for Enhancing Security-Focused Decision Support Systems
Carolina Fernández-Martínez, Shuaib Siddiqui, Vanesa Daza
cs.CR · cs.AI · cs.LG
The adoption and integration of heterogeneous stacks in most of today's open-source based networks brings clear benefits like interoperability and availability of advanced features. Yet, on the other hand the increasing number of interconnecting components and moving parts requires maintaining an ever increasing base of interdisciplinary knowledge of different tools in different domains to ensure proper operation. To alleviate such efforts,...
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04
Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
Yuchen Ling, Shengcheng Yu, Zhenyu Chen, Chunrong Fang
cs.CR · cs.AI
Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments. This transition changes the nature of security risk. In agentic settings, failures are no longer limited to unsafe text generation. Untrusted content may redirect control flow, misuse tool privileges, corrupt persistent state, leak sensitive information, or...
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05
MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents
Yv Zhang, Hao Sun, Hao Fang, Kuofeng Gao, Fan Mo, Bin Chen, Shu-Tao Xia, Yaowei Wang
cs.CR · cs.LG
External memory has become a core component of modern web agents, enabling long-horizon reasoning through the retrieval of past experiences. However, this paradigm introduces a critical vulnerability: malicious content injected into memory can be persistently recalled and repeatedly influence agent behavior. In this work, we identify and systematically study multimodal memory poisoning, an overlooked yet practical attack surface in web-agent...
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06
Do LLMsMakeNeural Distinguishers Wise?
Tatsuya Sakagami, Masashi Hisai, Naoto Yanai
cs.CR · cs.LG
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...
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07
Post-Quantum Secure Federated DeFi for Inclusive Banking
Swati Sachan, Dale Fickett, Richard Buchinger, Theo Miller
cs.CR · cs.AI · cs.CE · q-fin.CP
Recent advances in error-corrected qubits have accelerated the timeline for practical quantum computing. It poses a threat to cryptographic primitives used to secure financial systems, government infrastructure, communication networks, and DeFi (Decentralized Finance) ecosystems. This paper introduces a post-quantum secure federated DeFi framework that enables inter-bank collaboration to improve the inclusivity of individuals underserved by...
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08
From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
Huong Nguyen, Mickaël Bettinelli, Amirhossein Ghaffari, Alexandre Benoit, Hong-Tri Nguyen, Susanna Pirttikangas, Lauri Lovén
cs.CR · cs.AI
Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clients to train a shared-task model collaboratively without exposing their local data. While being a key component in any learning system, data is also a primary source of vulnerabilities and challenges, and a major determinant of a stable and well-converged training. Existing FL reviews describe...
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09
A Hybrid Edge-Cloud Architecture for Low-Latency Entitlement Verification in Resource-Constrained Devices
Pravin Nagare, Aditya Sabbineni, Devendra Dahiphale, Faiz Gouri, Pratik Thantharate
cs.CR · cs.AR · cs.DC
As digital media consumption shifts toward large-scale Over-the-Top (OTT) platforms, the efficiency of the control plane, specifically entitlement and identity verification, has become a critical factor in user experience. Current architectures often rely on synchronous cloud-tethered validation flows that introduce significant latency, especially on resource-constrained consumer electronics. This paper proposes a Hybrid Edge-Cloud...
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10
Assessing Automated Prompt Injection Attacks in Agentic Environments
David Hofer, Edoardo Debenedetti, Florian Tramèr
cs.CR · cs.AI
Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted external data, yet automated attack methods--proven effective for jailbreaking--remain underexplored in realistic agentic settings. We present a comprehensive empirical evaluation of automated prompt injection attacks against LLM agents, adapting both white-box (GCG) and black-box (TAP) methods to the agentic setting within the AgentDojo framework. We...
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