cs.CR · 2026-09-22 · No. 121
Cryptography and Security, 2026-09-22.
8 new papers in cs.CR. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
8 entries-
01
Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection
Fernando Outeda, Gustavo Betarte, Juan Diego Campo, Fiorella Cravero
cs.CR · cs.AI
Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation through prompt injection and jailbreak attacks. Classifier-based guardrails, such as Prompt Guard 2, are widely used as a first line of defense against such attacks, but their internal decision logic is largely opaque to both defenders and attackers. This paper presents an exploratory case study that...
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02
Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity Analysis
Jiling Zhou, Aisvarya Adeseye, Antti Hakkala, Seppo Virtanen, Jouni Isoaho
cs.CR · cs.AI
Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasoning over complex and heterogeneous data. However, existing prompting approaches typically focus on eliciting reasoning without explicitly considering how intermediate reasoning steps are structurally organized. We introduce Security Reasoning Topology, which models reasoning through three...
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03
Trust in Edge-Enabled IoT Security: Features, Challenges and Research Directions
Esin Ece Aydın, Şerif Bahtiyar, Gürkan Gür
cs.CR · cs.AI · cs.CY · cs.ET · cs.NI
Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things (IoT). To support time-sensitive and resource-constrained applications, IoT systems nowadays increasingly rely on edge computing. This brings computation and decision-making closer to end devices. In edge-enabled IoT architecture, latency and communication overhead are reduced, but interactions...
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04
Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents
Anastasia Pustozerova, Eugene Bagdasarian, Luca Beurer-Kellner, Battista Biggio, Nico Ebert, David Filip, Marc...
cs.CR · cs.AI
AI agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents. As incident reporting becomes increasingly important for legal compliance, governance, accountability, and security; current frameworks must be adapted to the unique characteristics of AI agents. In this paper, two editorial authors compare AI systems and AI agents and, drawing on input from 23 experts in academia and...
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05
ActGov: Governing LLM Agent Actions via Policy-Constrained Validation
Kaiyuan Zhang, Yuke Peng, Ke Jiang, Yinqian Zhang
cs.CR · cs.AI
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user authorization. Existing defenses isolate injected content or constrain execution with predefined plans and static policies, but these approaches are brittle under dynamic workflows and scale poorly across extensible tool ecosystems. In this work, we present ActGov, a...
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06
SkelOT: Reusing AOT Compilation Across EVM Contract Families
Sipeng Xie, Qianhong Wu, Minghang Li, Qin Wang, Zhipeng Wang, Bo Qin
cs.CR · cs.DC · cs.ET
Ahead-of-time (AOT) compilers (e.g., revmc, evmone, and DTVM) for the Ethereum Virtual Machine (EVM) reuse compilation artifacts at contract-code-hash granularity. This granularity is poorly matched to real EVM workloads dominated by \emph{contract families}: factory-, proxy-, and template-driven deployments that share instruction structure but differ in a small set of embedded constants. Across four EVM chains (Base, Ethereum, BSC, and...
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07
Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection
Md. Asif Sajeed, Md. Nazrul Islam Mondal, Md Ashraful Hossen Akash
cs.CR · cs.AI · cs.LG
Malware detection is a critical task in cybersecurity, and graph neural networks over control flow graphs have shown promising results for it. However, detectors are usually evaluated on a random split of a corpus collected over a single period, which cannot show how well a model generalizes to later samples. This study addresses that limitation with a strict temporal split: every model is trained on one period and scored once on a later one....
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08
From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models
Jiaxin Hong, Yuxin Peng, Hongyao Yu, Hao Fang, Shuoyang Sun, Bin Chen
cs.CR · cs.AI
Open-weight large language models (LLMs) can be copied, modified, and redeployed behind black-box APIs, making post-release ownership verification difficult. Existing black-box fingerprints often rely on secret query-key pairs that reproduce predefined responses, and can therefore be easily disrupted by fine-tuning, pruning, quantization, model merging, and serving-time prompt changes. We propose SimPrint, a recoverable semantic...
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