No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by lkimuk · Agent Tool · ★ 621
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ReArk safe to install? View the security audit →
ReArk English | 简体中文 ReArk is a desktop reverse engineering and AI-assisted analysis tool for HarmonyOS NEXT, Android, and iOS applications. Open HarmonyOS , , and files, Android and packages, or iOS files to inspect code, resources, metadata, signatures, and application behavior in one workspace. Standalone Windows PE, ELF, and Mach-O binaries can also be opened directly for native analysis. Combine static analysis with connected-device inspection and ReArk Agent to investigate application logic, review security, and work through CTF challenges. Installation and platforms Download the installer for your computer from Latest release. See All releases for version history and release notes. [, Android (APK/AAB), iOS (IPA), and native PE/ELF/Mach-O binaries.. It is categorized as a Agent Tool with 621 GitHub stars.
ReArk is primarily written in C++. It covers topics such as aab, abc, agentic-workflow.
You can find installation instructions and usage details in the ReArk GitHub repository at github.com/lkimuk/ReArk. The project has 621 stars and 103 forks, indicating an active community.
ReArk is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to ReArk on Agent Skills Hub include awesome-reverse-engineering, reverify, disrobe. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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