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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Virtual Reality and Augmented Reality Security: A Reconna...
Sarina Dastgerdy · 2024-07-23 · via cs.CR updates on arXiv.org

Various industries have widely adopted Virtual Reality (VR) and Augmented Reality (AR) technologies to enhance productivity and user experiences. However, their integration introduces significant security challenges. This systematic literature review focuses on identifying devices used in AR and VR technologies and specifies the associated vulnerabilities, particularly during the reconnaissance phase and vulnerability assessment, which are critical steps in penetration testing. Following Kitchenham and Charters' guidelines, we systematically selected and analyzed primary studies. The reconnaissance phase involves gathering detailed information about AR and VR systems to identify potential attack vectors. In the vulnerability assessment phase, these vectors are analyzed to pinpoint weaknesses that malicious actors could exploit. Our findings reveal that AR and VR devices, such as headsets (e.g., HTC Vive, Oculus Quest), development platforms (e.g., Unity Framework, Google Cardboard SDK), and applications (e.g., Bigscreen VR, VRChat), are susceptible to various attacks, including remote code execution, cross-site scripting (XSS), eavesdropping, and man-in-the-room attacks. Specifically, the Bigscreen VR application exhibited severe vulnerabilities like remote code execution (RCE) via the 'Application.OpenURL' API, XSS in user inputs, and botnet propagation. Similarly, the Oculus Quest demonstrated susceptibility to side-channel attacks and ransomware. This paper provides a detailed overview of specific device vulnerabilities and emphasizes the importance of the initial steps in penetration testing to identify security weaknesses in AR and VR systems. By highlighting these vulnerabilities, we aim to assist researchers in exploring and mitigating these security challenges, ensuring the safe deployment and use of AR and VR technologies across various sectors.