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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
Measuring the Effectiveness of Privacy Policies for Voice...
Song Liao, Christin Wilson, Long Cheng, Hongxin Hu, Huixing Deng · 2020-07-29 · via cs.CR updates on arXiv.org

Voice Assistants (VA) such as Amazon Alexa and Google Assistant are quickly and seamlessly integrating into people's daily lives. The increased reliance on VA services raises privacy concerns such as the leakage of private conversations and sensitive information. Privacy policies play an important role in addressing users' privacy concerns and informing them about the data collection, storage, and sharing practices. VA platforms (both Amazon Alexa and Google Assistant) allow third-party developers to build new voice-apps and publish them to the app store. Voice-app developers are required to provide privacy policies to disclose their apps' data practices. However, little is known whether these privacy policies are informative and trustworthy or not on emerging VA platforms. On the other hand, many users invoke voice-apps through voice and thus there exists a usability challenge for users to access these privacy policies. In this paper, we conduct the first large-scale data analytics to systematically measure the effectiveness of privacy policies provided by voice-app developers on two mainstream VA platforms. We seek to understand the quality and usability issues of privacy policies provided by developers in the current app stores. We analyzed 64,720 Amazon Alexa skills and 2,201 Google Assistant actions. Our work also includes a user study to understand users' perspectives on VA's privacy policies. Our findings reveal a worrisome reality of privacy policies in two mainstream voice-app stores, where there exists a substantial number of problematic privacy policies. Surprisingly, Google and Amazon even have official voice-apps violating their own requirements regarding the privacy policy.