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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
How Did That Get In My Phone? Unwanted App Distribution o...
Platon Kotzias, Juan Caballero, Leyla Bilge · 2020-10-20 · via cs.CR updates on arXiv.org

Android is the most popular operating system with billions of active devices. Unfortunately, its popularity and openness makes it attractive for unwanted apps, i.e., malware and potentially unwanted programs (PUP). In Android, app installations typically happen via the official and alternative markets, but also via other smaller and less understood alternative distribution vectors such as Web downloads, pay-per-install (PPI) services, backup restoration, bloatware, and IM tools. This work performs a thorough investigation on unwanted app distribution by quantifying and comparing distribution through different vectors. At the core of our measurements are reputation logs of a large security vendor, which include 7.9M apps observed in 12M devices between June and September 2019. As a first step, we measure that between 10% and 24% of users devices encounter at least one unwanted app, and compare the prevalence of malware and PUP. An analysis of the who-installs-who relationships between installers and child apps reveals that the Play market is the main app distribution vector, responsible for 87% of all installs and 67% of unwanted app installs, but it also has the best defenses against unwanted apps. Alternative markets distribute instead 5.7% of all apps, but over 10% of unwanted apps. Bloatware is also a significant unwanted app distribution vector with 6% of those installs. And, backup restoration is an unintentional distribution vector that may even allow unwanted apps to survive users' phone replacement. We estimate unwanted app distribution via PPI to be smaller than on Windows. Finally, we observe that Web downloads are rare, but provide a riskier proposition even compared to alternative markets.