https://github.com/threadlinee/ai-powered
AI-Powered Autonomous Penetration Testing Agent An advanced AI-driven cybersecurity tool that autonomously performs comprehensive penetration testing using reinforcement learning to improve attack strategies over time.
https://github.com/threadlinee/ai-powered
ai educational educational-project github github-config hacking-tool helpful lua python viral
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AI-Powered Autonomous Penetration Testing Agent An advanced AI-driven cybersecurity tool that autonomously performs comprehensive penetration testing using reinforcement learning to improve attack strategies over time.
- Host: GitHub
- URL: https://github.com/threadlinee/ai-powered
- Owner: Threadlinee
- Created: 2025-07-13T12:36:31.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-07-13T12:54:23.000Z (7 months ago)
- Last Synced: 2025-07-13T14:37:59.791Z (7 months ago)
- Topics: ai, educational, educational-project, github, github-config, hacking-tool, helpful, lua, python, viral
- Language: Python
- Homepage: https://docs.github.com
- Size: 187 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI-Powered Autonomous Penetration Testing Agent
An advanced AI-driven cybersecurity tool that autonomously performs comprehensive penetration testing using reinforcement learning to improve attack strategies over time.
## 🚀 Features
- **Autonomous Reconnaissance**: Automated target discovery and information gathering
- **Intelligent Vulnerability Scanning**: AI-powered vulnerability detection and assessment
- **Automated Exploitation**: Smart exploit selection and execution
- **Privilege Escalation**: Post-exploitation capabilities with AI guidance
- **Reinforcement Learning**: Continuously improves attack strategies based on success rates
- **Comprehensive Reporting**: Detailed attack paths with remediation advice
- **Ethical Safeguards**: Built-in safety mechanisms and authorization controls
## ⚠️ Important Disclaimer
This tool is designed for **authorized security testing only**. Users must:
- Have explicit permission to test the target systems
- Comply with all applicable laws and regulations
- Use only on systems they own or have written authorization to test
- Follow responsible disclosure practices
**The developers are not responsible for any misuse of this tool.**
## 🏗️ Architecture
```
AI-Powered Pentest Agent/
├── core/ # Core agent architecture
├── modules/ # Specialized testing modules
│ ├── reconnaissance/ # Target discovery & info gathering
│ ├── vulnerability/ # Vulnerability scanning
│ ├── exploitation/ # Exploit execution
│ └── privilege/ # Privilege escalation
├── ai/ # AI and ML components
│ ├── rl_agent/ # Reinforcement learning agent
│ ├── strategy/ # Attack strategy optimization
│ └── decision/ # Decision making engine
├── reporting/ # Report generation and analysis
├── config/ # Configuration and safety settings
└── api/ # REST API interface
```
## 🛠️ Installation
1. **Clone the repository**:
```bash
git clone
cd AI-Powered-Pentest-Agent
```
2. **Install dependencies**:
```bash
pip install -r requirements.txt
```
3. **Set up configuration**:
```bash
cp config/config.example.yaml config/config.yaml
# Edit config.yaml with your settings
```
4. **Initialize the database**:
```bash
python scripts/init_db.py
```
## 🚀 Quick Start
### Basic Usage
```python
from core.agent import PentestAgent
# Initialize the agent
agent = PentestAgent(
target="example.com",
scope=["web", "network"],
max_depth=3
)
# Run autonomous penetration test
results = agent.run_autonomous_test()
# Generate report
agent.generate_report("pentest_report.html")
```
### API Usage
```bash
# Start the API server
python api/main.py
# Run a test via API
curl -X POST "http://localhost:8000/api/v1/test" \
-H "Content-Type: application/json" \
-d '{"target": "example.com", "scope": ["web"]}'
```
## 📊 AI Components
### Reinforcement Learning Agent
- **Environment**: Custom gym environment simulating penetration testing scenarios
- **Algorithm**: PPO (Proximal Policy Optimization) for stable learning
- **Rewards**: Based on successful exploits, privilege escalation, and data access
- **State Space**: Target information, discovered vulnerabilities, current access level
- **Action Space**: Available exploits, reconnaissance techniques, privilege escalation methods
### Strategy Optimization
- **Attack Path Planning**: AI-driven selection of optimal attack sequences
- **Resource Allocation**: Intelligent distribution of testing resources
- **Adaptive Techniques**: Dynamic adjustment based on target responses
## 🔧 Configuration
Key configuration options in `config/config.yaml`:
```yaml
# Safety and Ethics
safety:
require_authorization: true
max_scan_intensity: "medium"
rate_limiting: true
# AI Settings
ai:
rl_learning_rate: 0.0003
exploration_rate: 0.1
model_update_frequency: 100
# Testing Scope
scope:
web_application: true
network_infrastructure: true
social_engineering: false
physical_security: false
```
## 📈 Reporting
The agent generates comprehensive reports including:
- **Executive Summary**: High-level findings and risk assessment
- **Technical Details**: Specific vulnerabilities and exploitation paths
- **Attack Timeline**: Chronological sequence of successful attacks
- **Remediation Advice**: Prioritized recommendations for fixing issues
- **AI Insights**: Learning outcomes and strategy improvements
## 🔒 Security Features
- **Authorization Verification**: Ensures proper permissions before testing
- **Rate Limiting**: Prevents overwhelming target systems
- **Audit Logging**: Complete trail of all actions taken
- **Safe Mode**: Non-destructive testing options
- **Emergency Stop**: Immediate halt capability
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request
## 📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 🆘 Support
For support and questions:
- Create an issue in the repository
- Check the documentation in `/docs`
- Review the configuration examples
## 🔮 Roadmap
- [ ] Advanced social engineering capabilities
- [ ] Cloud infrastructure testing
- [ ] Mobile application security
- [ ] IoT device testing
- [ ] Integration with SIEM systems
- [ ] Real-time threat intelligence
- [ ] Automated remediation suggestions