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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
Usability of Privacy Controls in Top Health Websites
Ravin Gunawardena, Yuemeng Yin, Yi Huang, Rahat Masood, Suranga · 2023-03-03 · via cs.CR updates on arXiv.org

With the increasing awareness and concerns around privacy, many service providers offer their users various privacy controls. Through these controls, users gain greater authority over the collection, utilisation, and dissemination of their personal information by the services. However, these controls may be buried deep within menus or settings, making them difficult for a user to access. Additionally, the terminology used to describe privacy controls can sometimes be confusing or technical, further complicating the user's ability to understand and use them effectively. This is especially true for health websites, as users often share sensitive information about their health and well-being. While many privacy controls have been proposed to protect user data on these sites, existing research focuses on individual controls (e.g., privacy policies or cookie opt-outs) rather than providing a comprehensive overview of the privacy landscape. In addition, many studies concentrate on the technical aspects of privacy controls without considering the usability of these features from a user's perspective. This paper aims to fill the gaps in the existing work by analysing four privacy controls, namely privacy nudge, privacy notice, privacy policy, and privacy setting, and evaluating their usability on the top 100 most visited health websites. First, we define usability attributes for each privacy control in three website visit scenarios; the guest, registering, and log-in visits. These attributes include awareness, efficiency, comprehension, functionality, and choice. Then, we design a survey template based on these attributes and scenarios and collect data about privacy controls. Next, we analyse the availability and usability of each privacy control on health websites. Finally, we provide suggestions for improving the design of these privacy controls based on the data analysis results.