limbosh

The honeypot shell that traps you in a conversation with an LLM pretending to be a shell.

Overview

Limbosh is named after the concept of Limbo, a liminal space where souls are held for eternity. In limbosh, likewise, the LLM will string attackers along for eternity (if it can!) in a fake shell, sandboxing them from interacting with the real system.

Prerequisites

To run limbosh out of the box, you'll need a few things set up on your machine first:

• Python 3.10 or higher with pip.
• Currently only Windows is supported.

<!-- * An OpenAI API key (get one [here](https://platform.openai.org)) -->

Setup

First, ensure that you've taken care of the prerequisites above. Then, from the root of the repository, set up a Python virtual environment:

python3 -m venv venv

Then, enter the virtual environment. On Windows, execute the following command in PowerShell:

./venv/bin/Activate.ps1

<!-- From bash on Mac or Linux, run: ```bash . venv/bin/activate ``` -->

From here, upgrade pip and install dependencies:

pip install --upgrade pip
pip install -r requirements.txt

Configuring OpenAI Connectivity

⚠️ Warning:
OpenAI connectivity is currently not supported.

• Dependencies likely need updating before integration will work.

<!-- The most performant way to run Limbosh is by outsourcing running the LLM to the OpenAI API. If you want to do this, you'll need to start by signing in to your [OpenAI platform account](https://platform.openai.com`), charging your account with credits, and creating an API key. First, copy the `config.json.example` file to `config.json`. Do not commit thie new file to source control (it's in `.gitignore` because it will contain your secret API key). ```bash cp config.json.example config.json ``` --> <!-- Now, open up `config.json` in your favourite text editor and paste your API key in place of `<your_api_key_here>`. Set the `model` field to either: * `gpt-3.5-turbo` for GPT 3.5 (faster and cheaper, not as convincing) * `gpt-4` for GPT 4.0 (slower and more expensive but with very convincing output) You're now ready to run the program! Either call the script with Python directly (make sure you're in your virtual environment): --> <!-- ```bash python3 limbosh.py ``` --> <!-- Or alternatively (on Mac or Linux), use the shortcut: --> <!-- ```bash # chmod u+x limbosh ./limbosh ``` -->

Running Against a Local LLM using Ollama

Limbosh also supports running against local LLMs using Ollama. To do this, you'll first need to install Ollama and pull your preferred LLM. You can do this like so for openchat for example:

ollama pull openchat

This will start the Ollama daemon process automatically, with its API listening on localhost:11434. Now, set up your config.json file like so. Note that openai_api_key is not needed for local LLM deployments on Ollama.

{
    "model_name": "openchat",
    "openai_api_key": "not-needed-for-local-llms",
    "system_prompt": "system_prompts/high_value_maritime_system.txt",
    "ollama": {
        "hostname": "localhost",
        "port": 11434
    }
}

Ollama has an extensive model library that you can install and try out with one ollama pull command.

Configuring System Prompts

You can find the system prompts that seed the LLM context in /system_prompts. The only system prompt included currently instructs the LLM to act as a bash shell on a high-value maritime system.

If you wish to create additional system prompts, simply create a new text file in /system_prompts and change the system_prompt key in config.json to point to this instead.

Deployment

You may wish to run a containerized version of limbosh in order to test it out or deploy it practically as a honeypot (don't do this yet, see vulnerabilities section below). To do so, first make sure you've configured OpenAI connectivity (see above) then build the container like so:

docker build . -t limbosh

Now run it, you'll be dropped into limbosh automatically. Note that forwarding port 11434 is only necessary if you're running an LLM locally on Ollama:

docker run --rm -d -p 2222:22 -p 11434:11434 limbosh

By default, a honeypot user admin (password also admin) will be created. SSH into it like so:

ssh -p 2222 admin@127.0.0.1

As soon as you connect and authenticate, you'll be dropped into a limbosh shell.

Using Docker Compose

If you'd like a complete setup out of the box, you can use Docker Compose. This involves a few more steps but keeps complexity to a minimum.

First, configure your config.json and get the Limbosh Docker Compose environment running (make sure to pass --build to carry across any config changes you've made):

docker compose up --build

This will bring up 3 containers:

• limbosh-honeypot: The Limbosh honeypot shell container. Port 22 is forwarded to port 2222 on the local machine, with honeypot username/password admin:admin.
• ollama: The Ollama container. Note that GPU acceleration is not set up (and is not even supported on Windows) so this will be extremely slow.
• ollama-webui: The Ollama web UI. You'll need to use this to install a model to query.

Once everything is up and running, browse to http://localhost:3000 in your browser. After creating an account (it's 100% local), you'll find yourself in the Ollama web UI, which looks a bit like the ChatGPT UI.

Click on your profile in the bottom left and browse to Settings > Models. From here, install whichever model you configured via your config.json file (openchat in this example):

This can take a while, so be patient. Once it's done, you can run the usual command to SSH into the honeypot:

ssh -p 2222 admin@127.0.0.1

Remember, on Windows, GPU acceleration is not available for Ollama on Docker. This means the CPU has to handle the full load of running the LLM, and this is very slow. You might have to wait a minute or more between entering a command and receiving something back!

Acknowledgements

The limbosh proof-of-concept was created by Saul Johnson (@lambdacasserole). Feel free to direct any questions, ideas, comments or contributions his way.