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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
Security Analysis of Top-Ranked mHealth Fitness Apps: An ...
Albin Forsberg, Leonardo Horn Iwaya · 2024-09-27 · via cs.CR updates on arXiv.org

Mobile health applications (mHealth apps), particularly in the health and fitness category, have experienced an increase in popularity due to their convenience and availability. However, this widespread adoption raises concerns regarding the security of the user's data. In this study, we investigate the security vulnerabilities of ten top-ranked Android health and fitness apps, a set that accounts for 237 million downloads. We performed several static and dynamic security analyses using tools such as the Mobile Security Framework (MobSF) and Android emulators. We also checked the server's security levels with Qualys SSL, which allowed us to gain insights into the security posture of the servers communicating with the mHealth fitness apps. Our findings revealed many vulnerabilities, such as insecure coding, hardcoded sensitive information, over-privileged permissions, misconfiguration, and excessive communication with third-party domains. For instance, some apps store their database API key directly in the code while also exposing their database URL. We found insecure encryption methods in six apps, such as using AES with ECB mode. Two apps communicated with an alarming number of approximately 230 domains each, and a third app with over 100 domains, exacerbating privacy linkability threats. The study underscores the importance of continuous security assessments of top-ranked mHealth fitness apps to better understand the threat landscape and inform app developers.