I have been spending a lot of time lately working with local AI models, and one of my favorite combinations has to be Ollama paired with the OpenClaw interface.
There is something incredibly satisfying about running powerful Large Language Models directly on your own hardware. However, with great power comes great responsibility—especially regarding network security. By default, local AI services can sometimes expose ports to untrusted networks if not configured carefully. That is why I created the Secure Ollama + OpenClaw Suite.
What is it?
This project is a set of deployment scripts designed to secure local Ollama and OpenClaw installations across Linux, Windows, and MacOS. It specifically remediates unauthenticated access vulnerabilities by forcing all traffic through a secure Reverse Proxy (Nginx, Caddy, or HAProxy) using Basic Authentication.
It automates the tedious parts of the setup process while ensuring strict security best practices, allowing you to focus on chatting with your models and building multi-agent systems.
Why OpenClaw?
OpenClaw provides a sleek, intuitive interface that makes interacting with Ollama incredibly seamless. When you combine the fast local processing of Ollama with the refined UI of OpenClaw, the experience is unmatched.
Core Security Features
To make this architecture robust, I built several security measures directly into the scripts:
- Localhost Binding: Automatically configures Ollama to bind strictly to
127.0.0.1. This makes the API invisible to untrusted networks and prevents direct unauthorized access on port 11434. - Reverse Proxy Gatekeeper: Acts as a single, secure entry point for both the Ollama API and the OpenClaw Dashboard.
- Basic Authentication: Enforces a strict username and password requirement before any data is exchanged with the backend services.
- Stream Optimization: Disables proxy buffering to ensure AI responses stream word-by-word without latency.
- CORS Handling: Automatically injects pre-configured headers, allowing the OpenClaw frontend to communicate perfectly with the Ollama backend.
Architecture Overview
Once deployed, your system will operate with the following secure routing:
- Ollama API: Runs on
127.0.0.1:11434(Internal loopback only) - OpenClaw Dashboard: Runs on
127.0.0.1:3000(Internal loopback only) - Public Entry Point: Port 80 / 443 (Controlled and authenticated by the Reverse Proxy)
Note: Inside the OpenClaw dashboard settings, you simply set your Ollama Host to http://127.0.0.1:11434. Since both services reside on the same machine, they communicate safely via the internal loopback interface.
Quick Start Guide
If you want to set this up yourself, here is how to get started in minutes.
1. Clone the repository ```bash git clone https://github.com/FaiGiJoon/secure-ollama-openclaw.sh.git cd secure-ollama-openclaw.sh
- Choose your platform
Linux (Nginx): Edit the MY_USER and MY_PASSWORD variables in the script first, then run:
Bash chmod +x TwoPathNginx.sh sudo ./TwoPathNginx.sh Windows (Caddy): Run in an Administrator PowerShell window:
PowerShell Set-ExecutionPolicy Bypass -Scope Process -Force .\setup_windows.ps1 -Domain “yourdomain.com” -Username “admin” -Password “YourSecurePassword” MacOS (Caddy):
Bash chmod +x setup_mac.sh sudo ./setup_mac.sh “yourdomain.com” “admin” “YourSecurePassword” Once the script finishes, access your dashboard at http://your-server-ip or your configured domain, where you will be prompted for your secure credentials.
What is Next? After putting in so many intensive hours recently into cybersecurity programming and helping clients build and secure their websites, I am beyond ready to step away from the keyboard for a bit.
In July, I am taking a massive step: I am leaving the Netherlands for a three-week vacation in New York, and I cannot wait! It is a big trip, and it is going to be a very well-deserved break. My itinerary consists entirely of hunting for Pokémon, finding great food, and hopefully catching some excellent weather for sunbathing.
I can’t wait to be playing so fun games for the Nintendo GameCube using dolphin as a emulator on my Mac and just have a on the go emulating machine that always has great benchmark for local ai projects. I will be bringing my hardware along to test local AI models in a new environment, but the primary goal is to rest, recharge, and enjoy the city.
If you are interested in local AI and security, check out the GitHub repository and let me know how it works for your setup. Now, I have a flight to catch.