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 LING71671 · MCP Server · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is open-reverselab safe to install? View the security audit →
ReverseLab Open-source reverse engineering lab — 197-article knowledge base, 100+ MCP automation tools, covering CTF pentesting / APK reverse engineering / PE binary analysis / cryptography & protocol cracking / game cheating analysis. Agent-native, directory-as-convention. 中文版 Routing Knowledge Base Each technique file follows this structure: Agent workflow: detect sig
| Stars | 1,191 |
| Forks | 284 |
| Language | Python |
| Category | MCP Server |
| License | GPL-3.0 |
| Quality Score | 66.9874129193503/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-29 |
| Created | 2026-06-17 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~14k |
These tools work well together with open-reverselab for enhanced workflows:
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open-reverselab is Open-source AI reverse-engineering agent platform and MCP server for Ghidra, Frida, x64dbg and Rizin — automated PE/APK/binary analysis, CTF and malware research, with 100+ MCP tools and a 194-article. It is categorized as a MCP Server with 1.2k GitHub stars.
open-reverselab is primarily written in Python. It covers topics such as ai-agent, ai-reverse-engineering, android-reverse-engineering.
You can find installation instructions and usage details in the open-reverselab GitHub repository at github.com/LING71671/open-reverselab. The project has 1.2k stars and 284 forks, indicating an active community.
open-reverselab is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to open-reverselab on Agent Skills Hub include ghidra-mcp, x64dbg-mcp-server, reagent. 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.
Sources & who's responsible: