KamelionStack-OSE - AI-powered pentesting framework with autonomous vu

项目说明

KameLionStack OSE

> AI-Powered Automated Penetration Testing - One Command, Complete Analysis

![Python 3.8+]()
![License: MIT]()

Stop clicking through tools manually. KameLionStack orchestrates your entire pentest workflow with AI-dri.

One command runs: Nmap → Nuclei → SQLMap → Nikto → ffuf → AI Analysis → Exploits. Automatically.

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What It Does

Real Results:

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Quick Start

Prerequisites

Python & Ollama:

AI Model Setup:

Choose based on your GPU:

| GPU VRAM | Recommended Model | Speed | Quality |
|----------|-------------------|-------|---------|
| 4GB or less | qwen2.5-coder:3b | 15-20 tok/sec | 85% |
| 6GB+ | qwen2.5-coder:7b | 30-40 tok/sec | 90% |
| 8GB+ | qwen2.5-coder:14b | 20-30 tok/sec | 95% |

Pentesting Tools (install what you need):

Check installed tools:

Installation

Run Your First Scan

Results saved to: Reports/enhanced_scan_report_[timestamp].json

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Scan Modes

| Mode | Time | What It Does | Best For |
|------|------|--------------|----------|
| quick | 2-3 min | Fast port scan + subdomains + Nuclei CVEs | Bug bounty recon |
| standard | 5-10 min | Full scan + all tools + SQLMap + AI analysis | Professional pentests |
| deep | 15-20 min | All 65K ports + extensi + e | Red team engagements |

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What Gets Tested

Automated Testing (43+ types)

Tools Orchestrated

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Usage Examples

Basic Scan

API Usage

Custom Configuration

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Example Output

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Architecture

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Configuration

Scan Modes:

Tool Modes:

AI Iterations: 1-20 (default: 10)

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Project Structure

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Legal Notice

For authorized testing only.

Unauthorized access is illegal. We're not responsible for misuse.

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Contributing

Pull requests welcome! Please:
1. Test your changes
2. Update documentation
3. Follow existing code style

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License

MIT License - See LICENSE file

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Credits

Built with:

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Made for security professionals by security amateurs supervised by Claude (who's actually pretty good at this stuff but won't admit it) 😂

*Disclaimer: No AI models were harmed in the making of this tool. Some were mildly confused about why we kept asking them to generate exploits, though.*

项目信息

开发语言：Python

Star 数：8

技术要点

该项目属于安全软件方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕AI-powered pentesting framework with autonomous vulnerability scanning, exploit generation, and tool orchestration.展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。