🤖 AI Summary
本文提出CLOADER框架，通过动态逃避策略和自定义加载器，解决移动安全环境中hook工具被检测的问题，实现90%的绕过率。
📝 Abstract
We propose a stealth framework that eliminates detection of hooking tools such as Frida and Xposed in secured mobile environments by replacing static configurations with dynamic evasion tactics. In contrast to existing approaches that apply these techniques independently, the framework introduces a unified runtime control layer that systematically coordinates network, temporal, and code-level evasive transformations. The solution integrates randomized port allocation, runtime code obfuscation, delayed execution triggers, and self-integrity checks to disrupt signature-based scans, timing heuristics, and tampering attempts. A custom Android loader, CLoader, enforces these mechanisms to isolate hooking activities from security monitors while maintaining complete interception and modification capabilities. Validation across enterprise anti malware systems, hardened applications, and device management platforms demonstrates a 90% bypass rate in our evaluation matrix. This approach enables reliable penetration testing and malware analysis in locked-down mobile ecosystems by masking network, temporal, and code-level fingerprints without architectural overhauls.