airgap-sns 0.0.1

Secure Notification System with audio capabilities

Airgap SNS (Secure Notification System)

A complete, fully-functional Python implementation of an extensible, modular notification framework tailored specifically for handling LLM outputs, notifications upon certain triggers, and secure communication between air-gapped devices.

Features

• Burst Sequence Parsing: Detect and parse special notification triggers in text
• WebSocket Pub/Sub: Real-time notification delivery via WebSockets
• Audio Transmission: Send data between air-gapped devices using sound (via ggwave)
• Encryption: Optional AES encryption for secure communication
• Webhooks: Integration with external systems via HTTP webhooks
• Water-cooler Channels: Broadcast notifications to groups of subscribers
• Interactive Client: Command-line interface for sending and receiving notifications
• Modular Architecture: Easily extensible for custom notification types and delivery methods

Dependencies

pip install fastapi uvicorn websockets aiohttp python-dotenv cryptography ggwave sounddevice numpy

For LLM integration:

# For OpenAI API
pip install openai

# For Ollama (local LLMs)
pip install httpx

For secure tunnel support (optional):

pip install zrok

Note:

• ggwave and sounddevice are optional dependencies for audio transmission features.
• zrok is an optional dependency for creating secure tunnels for remote connections.
• httpx is required for Ollama integration (local LLMs).

Project Structure

.
├── README.md
├── audio.py         # Audio transmission using ggwave
├── burst.py         # Burst sequence parsing
├── client.py        # Notification client
├── crypto.py        # Encryption utilities
├── host.py          # Notification host/server
├── scheduler.py     # Job scheduling
└── webhook.py       # Webhook integration

Burst Sequence Format

Burst sequences are special markers in text that trigger notifications:

!!BURST(dest=user123;wc=42;encrypt=yes;webhook=https://example.com/hook;audio=tx;pwd=secret)!!

Parameters:

• dest: Destination client ID
• wc: Water-cooler channel ID
• encrypt: Whether to encrypt the message (yes/no)
• webhook: URL to send a webhook notification
• audio: Audio transmission (tx/none)
• pwd: Optional password for encryption

Environment Variables

The system supports configuration via environment variables or a .env file. Create a .env file in the project root with the following variables:

# LLM Provider settings
# Choose between "openai" or "ollama"
LLM_PROVIDER=openai

# OpenAI settings (when LLM_PROVIDER=openai)
OPENAI_API_KEY=your_api_key_here
DEFAULT_MODEL=gpt-3.5-turbo

# Ollama settings (when LLM_PROVIDER=ollama)
OLLAMA_URL=http://localhost:11434
OLLAMA_MODEL=llama2
OLLAMA_STREAM=true

# Authentication key for chat clients
AUTH_KEY=demo-key

# Chat channel name
CHANNEL=demo-chat

# Server configuration
HOST=0.0.0.0
PORT=9000

# Enable/disable features
# Set to "true" to enable, anything else to disable
TUNNEL_ENABLED=false
RELOAD_ENABLED=false

A sample .env.sample file is provided as a template.

Usage

Starting the Server

Using the provided script:

# Make the script executable
chmod +x run_server.sh

# Start the server
./run_server.sh

# Start with secure tunnel for remote connections
./run_server.sh --tunnel-on

# Start with auto-reload for development
./run_server.sh --reload

Or manually:

uvicorn host:app --host 0.0.0.0 --port 9000

Running the Client

Basic usage:

python client.py --id user123

With interactive mode:

python client.py --id user123 --interactive

With password for decryption:

python client.py --id user123 --password mysecretpassword

Disable audio features:

python client.py --id user123 --no-audio

Interactive Client Commands

• /quit - Exit the client
• /audio <message> - Send message via audio
• /burst dest=<id>;wc=<channel>;... - Send custom burst
• /help - Show help

Testing the System

The project includes several test scripts to verify functionality:

Quick Demo

For a quick demonstration of the system, use the provided shell scripts:

Basic Demo

# Make the script executable (if not already)
chmod +x run_demo.sh

# Run the demo
./run_demo.sh

This script uses tmux to start multiple components in separate windows:

• Notification server
• Webhook test server
• Receiver client (interactive mode)
• Sender client (interactive mode)

Chat Demo

# Make the script executable (if not already)
chmod +x run_chat_demo.sh

# Run the chat demo
./run_chat_demo.sh

# Run with secure tunnel for remote connections
./run_chat_demo.sh --tunnel-on

This script starts a multi-user chat environment with:

• Notification server
• LLM provider client (if OpenAI API key is set)
• Multiple chat clients
• Help window with instructions

You can then interact with the system by sending messages between clients.

Automated Tests

Run the automated test suite to verify core functionality:

# Start the server in one terminal
uvicorn host:app --host 0.0.0.0 --port 9000

# Run the tests in another terminal
python test_sns.py

# Include audio tests (requires ggwave and sounddevice)
python test_sns.py --test-audio

# Include webhook tests (requires webhook_test_server.py running)
python test_sns.py --test-webhook

Webhook Testing

To test webhook functionality, run the webhook test server:

# Start the webhook test server
python webhook_test_server.py --port 8000

# View received webhooks
curl http://localhost:8000/webhooks

LLM Integration Demo

Test integration with LLMs using the demo script:

# For OpenAI:
export LLM_PROVIDER=openai
export OPENAI_API_KEY=your_api_key_here
export DEFAULT_MODEL=gpt-3.5-turbo

# OR for Ollama (local LLMs):
export LLM_PROVIDER=ollama
export OLLAMA_MODEL=llama2
export OLLAMA_URL=http://localhost:11434

# Run the LLM integration demo
python llm_integration_demo.py

For the chat demo with Ollama:

# Make sure Ollama is running
ollama serve

# Run the chat demo with Ollama
export LLM_PROVIDER=ollama
./run_chat_demo.sh

Integration with LLMs

LLMs can be instructed to include burst sequences in their output to trigger notifications:

Here's your answer: The capital of France is Paris.

!!BURST(dest=user123;wc=geography;encrypt=no)!!

Audio Transmission

The system can transmit data between air-gapped devices using sound:

# Send a message via audio
python client.py --id sender --interactive
> /audio Hello from an air-gapped device!

Secure Tunnel for Remote Connections

The system supports creating secure tunnels for remote connections using zrok:

1. Install zrok:

   pip install zrok
   

2. Configure zrok (first time only):

   zrok login
   

3. Start the server or chat demo with tunnel enabled:

   ./run_server.sh --tunnel-on
   # or
   ./run_chat_demo.sh --tunnel-on
   

4. The tunnel URL will be displayed and saved to tunnel_connection.txt

5. On the remote machine, connect using the tunnel URL:

   python client.py --id remote-user --uri <TUNNEL_URL>
   # or for chat demo
   python chat_app.py --id remote-user --channel demo-chat --host <TUNNEL_URL> --auth-key demo-key
   

Security Considerations

• All WebSocket connections should be secured with TLS in production
• Passwords for encryption should be strong and securely managed
• Audio transmission is susceptible to eavesdropping in shared spaces
• When using secure tunnels, ensure you trust the tunnel provider

Example Use Cases

1. LLM Notifications: Get notified when an LLM completes a task or needs input
2. Air-gapped Communication: Transfer data between isolated systems
3. Secure Messaging: Send encrypted messages to specific recipients
4. Broadcast Alerts: Notify groups of users about important events
5. Webhook Integration: Trigger external systems based on notifications

License

MIT