🚀 BUGHUNTER AI PENTEST AGENT

Automated penetration testing agent with a cyber-inspired GUI, resource-aware task scheduling, and AI-assisted analysis.

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🔎 Short description

Automation tool for penetration testing with a GUI, resource-aware task queueing, and AI-assisted analysis. Designed for labs and authorized testing only.

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⚠️ Security / Legal Notice

Use only on systems you own or have explicit permission to test. Unauthorized scanning is illegal. This script can run intrusive tools and may send reports to an external API (configurable). Review the code and API keys before running.

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🧰 Key Features

• Resource-aware task queueing (CPU / RAM) with configurable limits.
• Tool resource profiles (estimated CPU/RAM/time consumption).
• GUI built with tkinter featuring a "cyberpunk" theme (resource monitor, queue, console).
• Integration with an AI service to analyze reports and suggest commands/actions.
• Per-round report generation and automated CVE extraction.

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📦 Requirements

• Python 3.7+ (3.9+ recommended)
• psutil, requests, and tkinter (see install_dependencies.py).
• Optional external pentesting tools for full functionality: nmap, sqlmap, nikto, nuclei, masscan, hydra, metasploit, etc. Install via your distro package manager.

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🚀 Quick Installation

1. Clone the repository:

git clone https://github.com/ARESHAmohanad/BugHunter-AI
cd BugHunter-AI

2. Install Python dependencies (included script):

python3 install_dependencies.py

(This script checks and installs psutil and requests. tkinter may need to be installed via system packages.)

3. (Optional) Install system pentest tools:

# Example on Debian/Ubuntu
sudo apt update
sudo apt install -y nmap sqlmap nikto masscan

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▶️ Quick start (run)

See QUICK_START.md for a visual walkthrough or run:

python3 AIlinuxV2.py

Enter the target (authorized targets only), adjust Max Concurrent Tools, CPU/RAM thresholds, and click INITIATE SCAN in the GUI.

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⚙️ Important Configuration

• Configure API keys (if using AI integration) at the top of AIlinuxV2.py: API_KEYS and API_URL. The integration can send reports to an external endpoint — review privacy implications before sending sensitive data.
• Adjust Max Concurrent Tools, CPU Threshold, and RAM Threshold in the GUI to avoid overloading your host.

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🗂️ Output structure (example)

Each session produces a directory AI_Pentest_YYYYMMDD_HHMMSS_<id>/ with per-round subfolders:

AI_Pentest_YYYYMMDD_HHMMSS_ID/
├─ round_1/
│  ├─ nmap.txt
│  ├─ nikto.txt
│  ├─ report.json
│  └─ decision.json
├─ round_2/
├─ errors.log
└─ final_report.json

Reports and decisions are used by the AI engine to determine next steps.

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🛠️ How it works (technical summary)

1. User starts a scan and enqueues tools.
2. ResourceMonitor checks CPU/RAM and permits execution only when safe.
3. ToolQueueManager manages concurrent execution (configurable).
4. Outputs are aggregated into report.json.
5. The aggregated results are optionally sent to the AI engine; the AI returns commands inside <COMMANDS> and a <DECISION> JSON that lists next tools.

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📚 Documentation

• QUICK_START.md — quick walkthrough and examples.
• OPTIMIZATION_SUMMARY.md — summary of resource-usage optimizations (queueing, profiles, monitor).

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🤝 Contributing

Contributions welcome. Ideas:

• Add new tool resource profiles
• Improve AI response validation and command sanitization
• Add Docker/container support for isolated execution

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🧾 License

For educational use / authorized testing. Add a license file (MIT, Apache-2.0, etc.) as desired.

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NOTE: Review the code before running in any production environment. For safe usage, run inside an isolated VM against authorized labs or CTFs.