{"id":30815222,"url":"https://github.com/threadlinee/ai-powered","last_synced_at":"2026-05-09T05:34:36.190Z","repository":{"id":304498300,"uuid":"1018958272","full_name":"Threadlinee/AI-Powered","owner":"Threadlinee","description":"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.","archived":false,"fork":false,"pushed_at":"2025-07-13T12:54:23.000Z","size":191,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-13T14:37:59.791Z","etag":null,"topics":["ai","educational","educational-project","github","github-config","hacking-tool","helpful","lua","python","viral"],"latest_commit_sha":null,"homepage":"https://docs.github.com","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Threadlinee.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-07-13T12:36:31.000Z","updated_at":"2025-07-13T13:17:23.000Z","dependencies_parsed_at":"2025-07-13T14:48:16.456Z","dependency_job_id":null,"html_url":"https://github.com/Threadlinee/AI-Powered","commit_stats":null,"previous_names":["threadlinee/ai-powered"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/Threadlinee/AI-Powered","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Threadlinee%2FAI-Powered","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Threadlinee%2FAI-Powered/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Threadlinee%2FAI-Powered/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Threadlinee%2FAI-Powered/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Threadlinee","download_url":"https://codeload.github.com/Threadlinee/AI-Powered/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Threadlinee%2FAI-Powered/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273874160,"owners_count":25183368,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-06T02:00:13.247Z","response_time":2576,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","educational","educational-project","github","github-config","hacking-tool","helpful","lua","python","viral"],"created_at":"2025-09-06T08:09:44.121Z","updated_at":"2026-05-09T05:34:31.157Z","avatar_url":"https://github.com/Threadlinee.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AI-Powered Autonomous Penetration Testing Agent\n\nAn advanced AI-driven cybersecurity tool that autonomously performs comprehensive penetration testing using reinforcement learning to improve attack strategies over time.\n\n## 🚀 Features\n\n- **Autonomous Reconnaissance**: Automated target discovery and information gathering\n- **Intelligent Vulnerability Scanning**: AI-powered vulnerability detection and assessment\n- **Automated Exploitation**: Smart exploit selection and execution\n- **Privilege Escalation**: Post-exploitation capabilities with AI guidance\n- **Reinforcement Learning**: Continuously improves attack strategies based on success rates\n- **Comprehensive Reporting**: Detailed attack paths with remediation advice\n- **Ethical Safeguards**: Built-in safety mechanisms and authorization controls\n\n## ⚠️ Important Disclaimer\n\nThis tool is designed for **authorized security testing only**. Users must:\n- Have explicit permission to test the target systems\n- Comply with all applicable laws and regulations\n- Use only on systems they own or have written authorization to test\n- Follow responsible disclosure practices\n\n**The developers are not responsible for any misuse of this tool.**\n\n## 🏗️ Architecture\n\n```\nAI-Powered Pentest Agent/\n├── core/                    # Core agent architecture\n├── modules/                 # Specialized testing modules\n│   ├── reconnaissance/     # Target discovery \u0026 info gathering\n│   ├── vulnerability/      # Vulnerability scanning\n│   ├── exploitation/       # Exploit execution\n│   └── privilege/          # Privilege escalation\n├── ai/                     # AI and ML components\n│   ├── rl_agent/          # Reinforcement learning agent\n│   ├── strategy/           # Attack strategy optimization\n│   └── decision/           # Decision making engine\n├── reporting/              # Report generation and analysis\n├── config/                 # Configuration and safety settings\n└── api/                    # REST API interface\n```\n\n## 🛠️ Installation\n\n1. **Clone the repository**:\n   ```bash\n   git clone \u003crepository-url\u003e\n   cd AI-Powered-Pentest-Agent\n   ```\n\n2. **Install dependencies**:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Set up configuration**:\n   ```bash\n   cp config/config.example.yaml config/config.yaml\n   # Edit config.yaml with your settings\n   ```\n\n4. **Initialize the database**:\n   ```bash\n   python scripts/init_db.py\n   ```\n\n## 🚀 Quick Start\n\n### Basic Usage\n\n```python\nfrom core.agent import PentestAgent\n\n# Initialize the agent\nagent = PentestAgent(\n    target=\"example.com\",\n    scope=[\"web\", \"network\"],\n    max_depth=3\n)\n\n# Run autonomous penetration test\nresults = agent.run_autonomous_test()\n\n# Generate report\nagent.generate_report(\"pentest_report.html\")\n```\n\n### API Usage\n\n```bash\n# Start the API server\npython api/main.py\n\n# Run a test via API\ncurl -X POST \"http://localhost:8000/api/v1/test\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"target\": \"example.com\", \"scope\": [\"web\"]}'\n```\n\n## 📊 AI Components\n\n### Reinforcement Learning Agent\n- **Environment**: Custom gym environment simulating penetration testing scenarios\n- **Algorithm**: PPO (Proximal Policy Optimization) for stable learning\n- **Rewards**: Based on successful exploits, privilege escalation, and data access\n- **State Space**: Target information, discovered vulnerabilities, current access level\n- **Action Space**: Available exploits, reconnaissance techniques, privilege escalation methods\n\n### Strategy Optimization\n- **Attack Path Planning**: AI-driven selection of optimal attack sequences\n- **Resource Allocation**: Intelligent distribution of testing resources\n- **Adaptive Techniques**: Dynamic adjustment based on target responses\n\n## 🔧 Configuration\n\nKey configuration options in `config/config.yaml`:\n\n```yaml\n# Safety and Ethics\nsafety:\n  require_authorization: true\n  max_scan_intensity: \"medium\"\n  rate_limiting: true\n  \n# AI Settings\nai:\n  rl_learning_rate: 0.0003\n  exploration_rate: 0.1\n  model_update_frequency: 100\n  \n# Testing Scope\nscope:\n  web_application: true\n  network_infrastructure: true\n  social_engineering: false\n  physical_security: false\n```\n\n## 📈 Reporting\n\nThe agent generates comprehensive reports including:\n\n- **Executive Summary**: High-level findings and risk assessment\n- **Technical Details**: Specific vulnerabilities and exploitation paths\n- **Attack Timeline**: Chronological sequence of successful attacks\n- **Remediation Advice**: Prioritized recommendations for fixing issues\n- **AI Insights**: Learning outcomes and strategy improvements\n\n## 🔒 Security Features\n\n- **Authorization Verification**: Ensures proper permissions before testing\n- **Rate Limiting**: Prevents overwhelming target systems\n- **Audit Logging**: Complete trail of all actions taken\n- **Safe Mode**: Non-destructive testing options\n- **Emergency Stop**: Immediate halt capability\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests for new functionality\n5. Submit a pull request\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## 🆘 Support\n\nFor support and questions:\n- Create an issue in the repository\n- Check the documentation in `/docs`\n- Review the configuration examples\n\n## 🔮 Roadmap\n\n- [ ] Advanced social engineering capabilities\n- [ ] Cloud infrastructure testing\n- [ ] Mobile application security\n- [ ] IoT device testing\n- [ ] Integration with SIEM systems\n- [ ] Real-time threat intelligence\n- [ ] Automated remediation suggestions ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthreadlinee%2Fai-powered","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthreadlinee%2Fai-powered","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthreadlinee%2Fai-powered/lists"}