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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
Analyzing Privacy Implications of Data Collection in Andr...
Bulut Gözübüyük, Brian Tang, Kang G. Shin, Mert D. Pesé · 2024-09-24 · via cs.CR updates on arXiv.org

Modern vehicles have become sophisticated computation and sensor systems, as evidenced by advanced driver assistance systems, in-car infotainment, and autonomous driving capabilities. They collect and process vast amounts of data through various embedded subsystems. One significant player in this landscape is Android Automotive OS (AAOS), which has been integrated into over 100M vehicles and has become a dominant force in the in-vehicle infotainment market. With this extensive data collection, privacy has become increasingly crucial. The volume of data gathered by these systems raises questions about how this information is stored, used, and protected, making privacy a critical issue for manufacturers and consumers. However, very little has been done on vehicle data privacy. This paper focuses on the privacy implications of AAOS, examining the exact nature and scope of data collection and the corresponding privacy policies from the original equipment manufacturers (OEMs). We develop a novel automotive privacy analysis tool called PriDrive which employs three methodological approaches: network traffic inspection, and both static and dynamic analyses of Android images using rooted emulators from various OEMs. These methodologies are followed by an assessment of whether the collected data types were properly disclosed in OEMs and 3rd party apps' privacy policies (to identify any discrepancies or violations). Our evaluation on three different OEM platforms reveals that vehicle speed is collected at a sampling rate of roughly 25 Hz. Other properties such as model info, climate & AC, and seat data are collected in a batch 30 seconds into vehicle startup. In addition, several vehicle property types were collected without disclosure in their respective privacy policies. For example, OEM A's policies only covers 110 vehicle properties or 13.02% of the properties found in our static analysis.