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 sjkim1127 · MCP Server · ★ 205
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Reversecore_MCP safe to install? View the security audit →
ReversecoreMCP 🇰🇷 한국어 (Korean) An enterprise-grade MCP (Model Context Protocol) server for AI-powered reverse engineering. Enables AI agents to perform comprehensive binary analysis through natural language commands. 📋 Prerequisites Ghidra (Required for Decompilation) Ghidra is required for advanced decompilation features. The installation scripts automatically install Ghidra to directory. Option 1: Automatic Installation (Recommended) bash Linux/macOS chmod +x ./scripts/install-ghidra.sh ./scripts/install-ghidra.sh With
| Stars | 205 |
| Forks | 22 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.5267373475747/100 |
| Open Issues | 29 |
| Last Updated | 2026-10-01 |
| Created | 2025-11-10 |
| Platforms | mcp, python |
| Est. Tokens | ~18k |
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Reversecore_MCP is A security-first MCP server that empowers AI agents to perform automated reverse engineering, malware analysis, forensics, vulnerability research, and SAST — powered by Radare2, YARA, LIEF, Capstone, . It is categorized as a MCP Server with 205 GitHub stars.
Reversecore_MCP is primarily written in Python. It covers topics such as mcp, mcp-security, mcp-server.
You can find installation instructions and usage details in the Reversecore_MCP GitHub repository at github.com/sjkim1127/Reversecore_MCP. The project has 205 stars and 22 forks, indicating an active community.
Reversecore_MCP is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Reversecore_MCP on Agent Skills Hub include agent-security-scanner-mcp, code-pathfinder, medusa. 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: