🧠 NeuroSploit

NeuroSploit is an AI-powered offensive security agent designed to automate penetration testing tasks.
It is built on ChatGPT-5 (with support for other LLMs in the future) and aims to fully solve the Damn Vulnerable Web Application (DVWA) across all difficulty levels.

The goal of NeuroSploit is to provide an intelligent, modular, and automated assistant for pentesters, researchers, and Red Team operators.

⚡ Features

AI-driven exploitation using prompt-engineered reasoning.
Modular skill system (e.g., xss_dom_low, sqli_blind_high).
Support for multiple LLM backends (default: ChatGPT-5).
Designed to autonomously solve 100% of DVWA.
Extensible for real-world pentesting labs.

🛠️ Installation

1. Clone the repository

git clone https://github.com/yourname/NeuroSploit.git
cd NeuroSploit

2. Create a virtual environment (recommended)

python3 -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

🐐 Setting up DVWA

To test NeuroSploit locally, you need DVWA (Damn Vulnerable Web Application).

1. Install DVWA using Docker

git clone https://github.com/digininja/DVWA.git
cd DVWA
docker build -t dvwa .
docker run -it -p 80:80 dvwa

DVWA should now be available at: 👉 http://localhost/DVWA

Default credentials:

Username: admin
Password: password

2. Configure DVWA

Log in at http://localhost/DVWA/login.php
Navigate to Setup / Reset Database
Click Create / Reset Database
Set the DVWA Security Level (Low, Medium, High, Impossible) from the DVWA Security tab.

🚀 Usage

Example command:

python3 -m src.run --target 'http://localhost/DVWA' --skill xss_dom_low

This tells NeuroSploit to:

Use the AI agent backend (ChatGPT-5 by default).
Target http://localhost/DVWA.
Execute the XSS DOM-based (Low security) exploitation module.

📂 Project Structure

NeuroSploit/
│── src/
│   ├── run.py          # Main entrypoint
│   ├── agents/         # AI agents
│   ├── skills/         # Exploitation modules (XSS, SQLi, CSRF, etc.)
│   └── utils/          # Helpers (HTTP requests, parsing, logging)
│
│── requirements.txt
│── README.md

Offline LLM Support (LLaMA)

curl -fsSL https://ollama.com/install.sh | sh
ollama list
ollama serve
ollama pull llama3.2:1b
export MODEL_PROVIDER=ollama
export LLAMA_MODEL=llama3.2:1b
export LLAMA_BASE_URL=http://localhost:11434
curl -s http://localhost:11434/api/generate \
 -d '{"model":"llama3.1","prompt":"Return JSON: {\"ok\":true}","stream":false,"options":{"temperature":0}}'

🔮 Roadmap

Add support for SQL Injection automation.
Expand to other vulnerable labs (bWAPP, Juice Shop, VulnHub).
Integration with Red Team C2 frameworks.
Offline LLM support (LLaMA, Falcon).

⚠️ Disclaimer

This project is intended for educational and research purposes only. Do not use it against systems without explicit permission.

Use responsibly. 🛡️

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