Security & Cryptography · Computer Science & AI
Cryptography & Security
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1,372 papers, from 4 September 2026 to 5 November 2026: 1,372 arXiv preprints.
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Newest papers, page 19
Model Exposure on One Device Endangers Others: Rethinking Edge Neural Network Security across Devices
Neural networks (NNs) are increasingly deployed on edge devices such as mobile and IoT platforms, offering low latency and improved privacy compared to cloud-based deployment.You Are the Trigger: Speaker-Activated Backdoors
Speech models are increasingly used as an interface to AI systems, from transcription tools to voice agents.Profile-DP: Differential Privacy for User-Controlled Profile Release in Personalized LLM Inference
Personalized large language models (LLMs) increasingly rely on profile-conditioned inference, pairing public requests with rich, user-specific facts.AutoRedClaw: Automated Red-Teaming for Claw-Style Agents
Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw.When Models Know but Can't Linearly Say: Relational Encoding Limits Linear Vulnerability Classification in Code LLMs
Large language models can detect vulnerable code, yet the form of vulnerability encoding in their representations remains unclear.Guided Diffusion for Trustworthy Synthetic Text Generation
The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently memorize and even directly output sensitive…A Hybrid Information Flow Analysis Framework for Android Hybrid Apps
The WebView interface in Android (hybrid) applications enables the native side (e.g., Java) to exchange data with the Web (e.g., JavaScript).Global Security Blind Spots: Translation as a Safety Bypass in Low-Resource Languages
Large language models are deployed globally, yet safety alignment and evaluation remain concentrated in English: harmful requests refused in English can elicit unsafe completions when translated…Quantum-Seeded Distributed Post-Quantum Group Signatures for 5G V2X Communications
Vehicle-to-Everything (V2X) communications rely on elliptic-curve signatures that a quantum adversary breaks and on pseudonym certificates whose anonymity rests on rotation rate, not cryptography.Orchestrating Key Scarcity: Global Capacity Allocation in Hybrid QKD-PQC Networks
This article presents a Distributed Key Management System (DKMS) that provisions quantum-safe symmetric encryption keys to applications across a network.Exposing Vulnerability Monoculture Through Systemic Adversarial Robustness
Beneath the apparent diversity of foundation models lies an increasingly homogeneous technical stack of common training pipelines, distillation methods, and data.AgenticVulHunter: Bringing Local Agentic Vulnerability Hunting to Your Terminal
Finding vulnerabilities early, before they can affect the production system, is important for reducing the risk of any software attacks.Detecting AI Training in Datacentre Power
AI governance agreements require methods to detect large model training runs on a monitored computing cluster, without trusting the operator.SoK: PIAtlas: Decomposing the Unbounded Prompt Injection Attack Space
Prompt injection is the dominant security risk for large-language-model (LLM) applications and agents, yet the field still lacks a coherent map of how these attacks are constructed.Policy Is Not Capability: Some Safety Benchmarks Measure Which Definition of "Unsafe" You Trained On
A guardrail classifier is reported as if unsafe were a property of text.Many Repairs, One Direction: Steering Code Models Toward Safer Cryptographic API Use
Cryptographic API misuse in large language model-generated code can compromise software security.Federated Learning for Malware Image Classification under Data Heterogeneity
Malware image classification (MIC), in which binary files are converted into visual representations and deep neural networks are trained to identify malware families, has recently emerged as an…AdaFedXplain: Adaptive Federated Learning with Explainable AI for Ransomware Detection
Attacks using ransomware have developed into complex multiphase campaigns that target vital infrastructure, resulting in annual damages of billions of dollars.On Pearl Blockchain's Proof of Useful Work: Systematization and Flexibility
We revisit the proof-of-useful-work (PoUW) scheme in Pearl blockchain's white paper (Pearl Research Team), giving a systematic sensitivity characterization as an alternative to its Jackpot policy…GreenNIDS: A Benchmark Workflow and Analysis Dataset for NetFlow Intrusion Detection Experiments
Comparing the predictive performance and computational cost of flow-based intrusion detection experiments requires both quantities to be available for each run.