惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
V2EX
酷 壳 – CoolShell
酷 壳 – CoolShell
美团技术团队
有赞技术团队
有赞技术团队
Hugging Face - Blog
Hugging Face - Blog
罗磊的独立博客
S
SegmentFault 最新的问题
D
Docker
博客园 - 司徒正美
雷峰网
雷峰网
V
Visual Studio Blog
云风的 BLOG
云风的 BLOG
G
Google Developers Blog
The GitHub Blog
The GitHub Blog
A
About on SuperTechFans
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
博客园 - Franky
月光博客
月光博客
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
H
Hackread – Cybersecurity News, Data Breaches, AI and More
T
The Blog of Author Tim Ferriss
Google DeepMind News
Google DeepMind News
MyScale Blog
MyScale Blog
MongoDB | Blog
MongoDB | Blog

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
Misbehavior in Mobile Application Markets
Steven Meyer · 2011-07-06 · via cs.CR updates on arXiv.org

Mobile application markets facilitate the distribution of applications and thus help developers advertise their work and customers find useful applications. In addition, the operators of mobile application markets can control the quality and the content of the applications. These markets are growing rapidly with more than 300'000 application in the App Store of Apple and more than 100'000 in the Android Market of Google. This is not only a great opportunity for phone manufacturers to earn money but also for indie developers (single or small teams of developers with small financial support) who can thus have a great distribution channel. Steve Demeter, the Trim game developer for iPhone, became millionaire with a single puzzle game . Obviously, as new markets generate a lot of money, the temptation of misbehavior to steal part of the benefits is big. The first famous case was the one of Molinker who self-rated his applications with 5 stars to pump up his ranking in order to increase its revenue stream. In this report, we will consider the problem of misbehavior in mobile application markets. We will investigate multiple attacks by misbehaving developers, users or network operators that aim at breaking rules for their own benefit, managing to outwit the operators' control on which applications can be installed. We notably suggest novel attacks that may affect mobile markets in the future: in particular, we show that it is possible to get revenue for applications created by someone else, trick a user to download and buy an application and new ways to pump up an application's ranking. We will also discuss possible solutions against spyware applications and cheating developer