Deploying Robin AI on M1 iMac

In the realm of cybersecurity and open-source intelligence (OSINT), tools that automate and refine dark web investigations are invaluable for network administrators and threat analysts. Robin AI stands out as an open-source solution that leverages large language models (LLMs) to streamline searches across Tor-hidden services, filtering out noise and scams while generating actionable reports. This post provides a comprehensive guide to deploying Robin on an Apple Silicon M1 iMac using Docker, ensuring compatibility and efficiency on ARM-based hardware. By containerizing the tool, you gain portability and isolation, allowing seamless integration into your workflow without environmental conflicts.

What is Robin AI?

Robin is an AI-powered OSINT framework designed specifically for ethical dark web reconnaissance. It addresses the challenges of Tor-based searches—such as dead links, honeypots, and irrelevant results—by employing LLMs (e.g., Llama 3 via Ollama) for query refinement, semantic filtering, and content summarization. Key capabilities include:

• Parallel querying of over 15 dark web search engines (e.g., Ahmia, Torch).
• AI-driven reduction of results from hundreds to a curated 10-20 high-value sources.
• Automated scraping and Markdown report generation, complete with visualizations and recommendations.

As an open-source project hosted on GitHub, Robin emphasizes privacy and ethics, running entirely over Tor with no data logging. It supports both command-line interface (CLI) and web-based UI modes, making it suitable for quick analyses or collaborative reviews. In a landscape where up to 90% of dark web “sites” are fraudulent, Robin’s semantic analysis provides a significant edge for threat hunting and compliance auditing.

Prerequisites

Before proceeding, ensure your M1 iMac meets these requirements:

• macOS Version: Ventura (13.0) or later for optimal Docker performance.
• Hardware: At least 8GB unified memory (16GB recommended for LLM inference).
• Tools:
• Docker Desktop for Apple Silicon (free for personal use).
• Homebrew package manager.
• Tor for anonymous routing.
• Ollama for local LLM hosting (optional but recommended for privacy).

Verify your setup by running sw_vers in Terminal to confirm macOS details and sysctl -n hw.optional.arm.FEAT_LSE to ensure ARM64 compatibility.

Step-by-Step Setup on M1 iMac

Follow these instructions in Terminal for a streamlined installation. The process leverages Docker’s native ARM support, avoiding emulation overhead.

1. Install Docker Desktop

• Download the Apple Silicon edition from docker.com.
• Open the .dmg file, drag Docker to Applications, and launch it.
• Complete the onboarding: Sign in with a free account and enable Kubernetes if desired (optional for Robin).
• Test: Run docker run hello-world to confirm functionality.

2. Install Tor

• If Homebrew is not installed: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)".
• Install Tor: brew install tor.
• Start the service: brew services start tor.
• Verify: tor --version should output the current version (e.g., 0.4.8.x as of late 2025).

Tor provides the SOCKS5 proxy (default port 9050) essential for Robin’s dark web access.

3. Install and Configure Ollama

• Download the M1-native binary from ollama.com.
• Install: sudo mv ollama /usr/local/bin/ (or use the installer).
• Pull Llama 3.1: ollama pull llama3.1:8b (lighter variant for M1; ~4GB download).
• Start the server: OLLAMA_HOST=0.0.0.0 ollama serve & (exposes on port 11434 for Docker access).
• Test: ollama run llama3.1 "Hello, world" to ensure inference works.

Ollama enables offline LLM processing, keeping sensitive queries local.

4. Prepare Environment Variables

• Create a .env file in your working directory:

  OLLAMA_BASE_URL=http://host.docker.internal:11434
  MODEL=llama3.1:8b

• For cloud LLMs (optional): Add API keys like OPENAI_API_KEY=your-key.

5. Pull and Run Robin Docker Image

Robin is distributed as a multi-arch Docker image, fully compatible with M1.

• Pull the latest: docker pull apurvsg/robin:latest.
• For Web UI Mode (recommended for beginners):

  docker run --rm \
    -v "$(pwd)/.env:/app/.env" \
    --add-host=host.docker.internal:host-gateway \
    -p 8501:8501 \
    apurvsg/robin:latest ui --ui-port 8501 --ui-host 0.0.0.0

• Access at http://localhost:8501.
• Enter a query (e.g., “ransomware forums”) and select your model.
• For CLI Mode:

  docker run --rm \
    -v "$(pwd)/.env:/app/.env" \
    --add-host=host.docker.internal:host-gateway \
    apurvsg/robin:latest cli --model llama3.1:8b --query "zero-day exploits" --threads 8

• Outputs a Markdown report in the current directory.

The --add-host flag ensures Docker can reach your local Ollama instance on M1. Expect 20-40 minutes for a full run, depending on query complexity.

Running Examples and Best Practices

Once deployed, test with benign queries to familiarize yourself:

• Basic Threat Intel: Query “latest phishing kits” – Robin refines to targeted terms, scans engines, and summarizes active markets.
• Report Customization: Use --output-dir ./reports to persist files; integrate with tools like Markdown viewers for sharing.

Best practices for network admins:

• Security: Always pair with a VPN; avoid personal data in queries.
• Performance: Limit threads to 75% CPU (--threads 6 on M1) to prevent thermal throttling.
• Ethics: Restrict to legal OSINT; review Robin’s built-in guardrails.
• Troubleshooting: Check logs with docker logs <container-id>. If Ollama fails, verify the server with curl http://localhost:11434.

For advanced use, explore Docker Compose for multi-container setups (Robin + persistent Tor).

Conclusion

Deploying Robin AI on an M1 iMac via Docker exemplifies the convergence of containerization and AI in cybersecurity operations. This setup not only democratizes dark web analysis but also ensures reproducible, secure workflows across your infrastructure. With minimal overhead on Apple Silicon, it’s an efficient addition to any threat hunting toolkit.