by ASCIT31 · MCP Server · ★ 783
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The Open-Source AI-Powered Autonomous Penetration Testing Platform Full Documentation · Contributing · License What is DarkMoon? DarkMoon is an automated penetration testing tool that orchestrates complete security assessments using artificial intelligence security agents. Built as an open-source cybersecurity tool, it enables organizations to run professional-grade vulnerability assessments without manual intervention. Instead of replacing the pentester, DarkMoon acts as an autonomous security testing system — it reasons, plans, and coordinates specialized agents that execute real offensive security operations through a controlled execution layer. Watch DarkMoon in action — Full autonomous penetration test demo Why DarkMoon? Traditional penetration testing is: ⏱️ Time-consuming — manual testing takes weeks 💰 Expensive — expert consultants cost thousands per day 🔄 Inconsistent — results var
| Stars | 783 |
| Forks | 131 |
| Language | Python |
| Category | MCP Server |
| License | GPL-3.0 |
| Quality Score | 68.6745253766074/100 |
| Open Issues | 2 |
| Last Updated | 2026-07-27 |
| Created | 2024-11-26 |
| Platforms | browser, k8s, mcp, python |
| Est. Tokens | ~16k |
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Dark-Moon is Autonomous AI pentesting engine, continuous offensive security across web, cloud, AD & Kubernetes. Agentic reasoning + real exploit execution deliver proof-based vulnerabilities. Privacy gateway: the . It is categorized as a MCP Server with 783 GitHub stars.
Dark-Moon is primarily written in Python. It covers topics such as active-directory, ai-agents, ai-red-team.
You can find installation instructions and usage details in the Dark-Moon GitHub repository at github.com/ASCIT31/Dark-Moon. The project has 783 stars and 131 forks, indicating an active community.
Dark-Moon is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to Dark-Moon on Agent Skills Hub include pentest-ai-agents, CyberStrike, numasec. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.