OmniGuard AI is an open-source fraud detection and cyber defense platform. It is designed to identify and neutralize social engineering attacks, phishing, and financial fraud across digital channels.
Features • Architecture • Installation • API • Contributing • License
- Threat Core: Pattern recognition for detecting digital threats, fake KYC, lottery bait, APK droppers, and courier imposter schemes.
- Threat Radar: Live scam feed, homoglyph link inspection, and QR/UPI debit analyzer.
- Audio Guard: Audio waveform visualizer and speech analyzer for voice cloning detection.
- Android APK Sandbox: Manifest privilege analyzer, Accessibility hijacking detector, and trojan profiler.
- Dossier Export: Formats incident data into structured evidence dossiers for legal submission.
- Multi-language Support: Localization in English, Hindi, Marathi, Spanish, French, German, Chinese, Japanese, Arabic, and Portuguese.
- Interactive Drills: Scenario simulator for training users to identify phishing and authority impersonation.
- Breach Intel: Checks compromised credentials, leaked hashes, and underground forum data.
- Audit Logging: Event telemetry and loss-prevention analytics with local state wipe capabilities.
OmniGuard AI uses a decoupled architecture with an asynchronous FastAPI backend and a React 19 + Vite frontend.
User -> React Frontend -> FastAPI Backend -> Heuristic Rule Engine
-> Threat Intel Correlator
OmniGuard AI/
├── app/ # FastAPI Backend Service
│ ├── main.py # API Gateway, routes & endpoints
│ ├── rules.py # Core scam engines & regex rules
│ └── models.py # Pydantic schemas & data models
├── web/ # React 19 Frontend
│ ├── src/
│ │ ├── components/ # Navigation and UI components
│ │ ├── pages/ # Scanner, Simulator, Logs, BreachMonitor
│ │ ├── i18n/ # Internationalization
│ │ ├── App.tsx # Root application shell & layout
│ │ └── index.css # Global stylesheet
│ ├── package.json # Frontend dependencies
│ └── vite.config.ts # Vite build configuration
├── eval/ # Benchmark & Evaluation Suite
├── data/ # Local threat signatures and seed datasets
├── tests/ # Automated tests
├── Dockerfile # Containerized deployment specification
├── requirements.txt # Python dependencies
└── README.md # Platform documentation
- Python 3.10+
- Node.js 18+
- Git
git clone https://github.com/Anurag-tech22/DEV1.git
cd DEV1python -m venv venv
# On Windows:
venv\Scripts\activate
# On Linux/macOS:
source venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --host 127.0.0.1 --port 8000 --reloadAPI documentation is accessible at http://127.0.0.1:8000/docs.
cd web
npm install
npm run devOpen your browser at http://localhost:5173/.
docker build -t omniguard-ai-security .
docker run -p 8000:8000 omniguard-ai-securityEvaluates an incoming message, SMS, email, or URL for fraud vectors.
Request:
{
"text": "URGENT: Electricity power will be disconnected tonight. Call officer at 9876543210 immediately.",
"lang": "en"
}Response:
{
"verdict": "scam",
"score": 92,
"type": "Utility Impersonation",
"explain": "High-urgency utility shutoff scam demanding immediate contact via an unverified personal phone number.",
"reasons": [
"Fabricated urgency demanding immediate action under threat of disconnection"
],
"actions": [
"Do not call the provided telephone number"
]
}Other available endpoints include /api/drill, /api/history, /api/intel, /api/url-inspect, /api/upi-inspect, /api/audio-inspect, and /api/apk-inspect. See the OpenAPI docs at /docs for complete details.
Run the automated evaluation suite against real-world benchmarks:
pytest
python eval/run.py- Data is evaluated locally in memory.
- No PII is sent to external proprietary commercial models.
- The platform can be self-hosted and operated in air-gapped environments.
Distributed under the MIT License. See LICENSE for more information.