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by fzn0x · Agent Tool · ★ 205
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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🏰 Watchtower (AI-Powered Penetration Testing Framework) []() Watchtower is a simple AI-powered penetration testing automation CLI tool that leverages LLMs and LangGraph to orchestrate agentic workflows that you can use to test your websites locally. Generate useful pentest reports for your websites. Penetration testing is red team activity and should be done with permission. ⚠️ Legal Disclaimer Watchtower is designed exclusively for authorized security testing and educational purposes. Legal Use: Authorized penetration testing, security research, educational environments. Illegal Use: Unauthorized access, malicious activities, any form of cyber attack. You are fully responsible for ensuring you have explicit written permission before testing any system. Unauthorized access to computer systems is illegal under laws including the Computer Fraud and Abuse Act (CFAA), GDPR, and equivalent internatio
| Stars | 205 |
| Forks | 33 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.7942899743299/100 |
| Last Updated | 2026-02-27 |
| Created | 2026-02-27 |
| Platforms | browser, claude-code, cli, python |
| Est. Tokens | ~8k |
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watchtower is Watchtower is a simple AI-powered penetration testing automation CLI tool that leverages LLMs and LangGraph to orchestrate agentic workflows that you can use to test your websites locally. Generate us. It is categorized as a Agent Tool with 205 GitHub stars.
watchtower is primarily written in Python. It covers topics such as ai-cybersecurity, automation-testing, claude.
You can find installation instructions and usage details in the watchtower GitHub repository at github.com/fzn0x/watchtower. The project has 205 stars and 33 forks, indicating an active community.
watchtower is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to watchtower on Agent Skills Hub include nyxstrike, tengu, mcp-security-hub. 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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