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by peter-hackertarget · LLM Plugin · ★ 101
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LLM-Nmap A plugin for Simon Willison's LLM tool that provides Nmap network scanning capabilities through function calling. This plugin enables LLMs to perform network discovery and security scanning tasks using the powerful Nmap tool. Head over to our blog for a full write up of the experiment. Features Network Discovery: Get local network information and suggested scan ranges Port Scanning: Scan specific ports or ranges on target hosts Service Detection: Identify services and versions running on open ports OS Detection: Attempt to identify target operating systems Ping Scanning: Discover live hosts on a network Script Scanning: Run Nmap NSE scripts for advanced detection Quick Scanning: Fast scans of common ports Prerequisites Python 3.7+ LLM - Simon Willison's command-line tool for Large Language Models Nmap - Network exploration tool and security scanner Installation Working LLM tool, ensure the tool and model plugins (llm-gemini) are updated: Working Nmap Install: Ubuntu/Debian: macOS: Windows: Download from nmap.org The llm-tools-nmap.py functions were created as a quick experiment. Simply launch using the --functions capability. bas
| Stars | 101 |
| Forks | 27 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 63.7280313659631/100 |
| Open Issues | 1 |
| Last Updated | 2025-06-12 |
| Created | 2025-06-12 |
| Platforms | python |
| Est. Tokens | ~1k |
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llm-tools-nmap is an open-source llm plugin by peter-hackertarget with 101 GitHub stars.
llm-tools-nmap is primarily written in Python.
You can find installation instructions and usage details in the llm-tools-nmap GitHub repository at github.com/peter-hackertarget/llm-tools-nmap. The project has 101 stars and 27 forks, indicating an active community.
llm-tools-nmap is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to llm-tools-nmap on Agent Skills Hub include codinit-dev, bpmn-assistant, lm-proxy. 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: