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cs.CR updates on arXiv.org

Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
Towards a Decentralized IoT Onboarding for Smart Homes Us...
Narges Dadkhah, Khan Reaz, Gerhard Wunder · 2025-08-29 · via cs.CR updates on arXiv.org

The increasing adoption of smart home devices and IoT-based security systems presents significant opportunities to enhance convenience, safety, and risk management for homeowners and service providers. However, secure onboarding-provisioning credentials and establishing trust with cloud platforms-remains a considerable challenge. Traditional onboarding methods often rely on centralized Public Key Infrastructure (PKI) models and manufacturer-controlled keys, which introduce security risks and limit the user's digital sovereignty. These limitations hinder the widespread deployment of scalable IoT solutions. This paper presents a novel onboarding framework that builds upon existing network-layer onboarding techniques and extends them to the application layer to address these challenges. By integrating consortium blockchain technology, we propose a decentralized onboarding mechanism that enhances transparency, security, and monitoring for smart home architectures. The architecture supports device registration, key revocation, access control management, and risk detection through event-driven alerts across dedicated blockchain channels and smart contracts. To evaluate the framework, we formally model the protocol using the Tamarin Prover under the Dolev-Yao adversary model. The analysis focuses on authentication, token integrity, key confidentiality, and resilience over public channels. A prototype implementation demonstrates the system's viability in smart home settings, with verification completing in 0.34 seconds, highlighting its scalability and suitability for constrained devices and diverse stakeholders. Additionally, performance evaluation shows that the blockchain-based approach effectively handles varying workloads, maintains high throughput and low latency, and supports near real-time IoT data processing.