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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
Fluid Antenna System-assisted Physical Layer Secret Key G...
Zhiyu Huang, Guyue Li, Hao Xu, Derrick Wing Kwan Ng · 2025-09-19 · via cs.CR updates on arXiv.org

This paper investigates physical-layer key generation (PLKG) in multi-antenna base station systems, by leveraging a fluid antenna system (FAS) to dynamically customize radio environments. Without requiring additional nodes or extensive radio frequency chains, the FAS effectively enables adaptive antenna port selection by exploiting channel spatial correlation to enhance the key generation rate (KGR) at legitimate nodes. To comprehensively evaluate the efficiency of the FAS in PLKG, we propose an FAS-assisted PLKG model that integrates transmit beamforming and sparse port selection under independent and identically distributed and spatially correlated channel models, respectively. Specifically, the PLKG utilizes reciprocal channel probing to derive a closed-form KGR expression based on the mutual information between legitimate channel estimates. Nonconvex optimization problems for these scenarios are formulated to maximize the KGR subject to transmit power constraints and sparse port activation. We propose an iterative algorithm by capitalizing on successive convex approximation and Cauchy-Schwarz inequality to obtain a locally optimal solution. A reweighted $\ell_1$-norm-based algorithm is applied to advocate for the sparse port activation of FAS-assisted PLKG. Furthermore, a low-complexity sliding window-based port selection is proposed to substitute reweighted $\ell_1$-norm method based on Rayleigh-quotient analysis. Simulation results demonstrate that the FAS-PLKG scheme significantly outperforms the FA-PLKG scheme in both independent and spatially correlated environments. The sliding window-based port selection method introduced in this paper has been shown to yield superior KGR, compared to the reweighted $\ell_1$-norm method. It is shown that the FAS achieves higher KGR with fewer RF chains through dynamic sparse port selection.