Installation Guide for AironmanGPT Project

AironmanGPT also known as Jarvis is an artificial intelligence assistant based on the initial work from @luijait designed to perform pentesting tasks.

The initial philosophy is to launch applications for each of the different phases, running wireshark/tshark at the same time to combine the two outputs and add them as input to the LLM, so that each output will give you its opinion.

For now, in the initial phases I am using nmap with different parameters, along with wireshark.

The system prompt for now only expects you to tell it the objective to investigate, which can be an IP or a range of IPs.

The app expects you to have both some dependencies like nmap, wireshark and more to be installed on your system.

The app lets you to run commands from your system and output the results to the llm. Just run command=your-command.

The app lets you to run command from your system and NOT output to the llm. Just run a command and see the results. For security shakes, i will permit just inofensive commands from the host.

The app lets you to talk with the system as a normal chatGPT. The output will be sent to the llm

The app lets you to target a specific ip/range, which is the one that you want to scan. Basically it will run a bunch of commands, output to the llm each output to feed it and give you the results. Just run target=your-target.

The system will use openAI and premAI as llms. Actually i recommend to use premAI and remm-slerp-l2-13b as llm. OpenAI is quite annoying, actually, and premAI provides a lot of different options. I will test it with differents other llm engines, like Mythalion-13b.

At some point I will create a make file to have all the dependencies ready. DONE!

At some point I will create a web interface to see the results, as well as to be able to properly export the results.

This guide will help you set up and run the project on your local environment.

Prerequisites

Before starting, make sure you have Python installed on your system. This project has been tested with Python 3.10 and higher versions. Make sure you have docker installed on your system. nmap wireshark curl

Environment Setup

1. Clone the Repository

   First, you need to clone the GitHub repository to your local machine. You can do this by executing the following command in your terminal:

  git clone https://github.com/alonsoir/AironmanGPT.git

  cd AironmanGPT

2. Configure Environment Variables

   Copy the .example.env file to a new file named .env:

   You will need to set up some environment variables for the script to work correctly. I had problems running the container with this format:

   OPENAI_API_KEY="API_KEY from openai.com"

   so i recommend you to use the following format, without double quotes:

   OPENAI_API_KEY=YOUR_OPENAI_APIKEY
   DEHASHED_API_KEY=""
   DEHASHED_USERNAME=""
   GPT_MODEL_NAME=gpt-3.5-turbo-0125
   GPT_MODEL_NAME_TEST=babbage-002
   INTERFACE_TSHARK=en0
   NMAP_OUTPUT_FILE=nmap_scan_results.xml
   PCAP_OUTPUT_FILE=captura_trafico.pcap
   # unnecessary, deprecated to be deleted
   TARGET_NETWORK=127.0.0.1
   INITIAL_WAIT_TIME=5
   CAPTURE_DURATION=10
   TEMPERATURE=1
   MAX_TOKENS=4096
   TIMEOUT=2
   MAX_RETRIES=2
   NGROK_AUTH_TOKEN=YOUR_NGROK_AUTH_TOKEN
   # zeroday-api/premai-api/openai-api
   DEFAULT_ENGINE=premai-api
   PREMAI_API_KEY=YOUR_PREMAI_API_KEY
   PREMAI_MODEL=remm-slerp-l2-13b
   PREMAI_PROJECT_ID=540
   PREMAI_TEMPERATURE=0.7
   PREMAI_SESSION_ID="my-session"
   PREMAI_SYSTEM_PROMPT="You are a helpful assistant."
   TIKTOKEN_ENCODING=cl100k_base
   USE_TSHARK=false
   #deprecated
   ZERODAY_API_KEY=YOUR_ZERODAY_API_KEY
   POSTGRES_DB=msf
   POSTGRES_USER=msf
   POSTGRES_PASSWORD=msf_password
   MSF_COMMAND_OSX=/opt/metasploit-framework/bin/msfconsole
   MSF_COMMAND=/usr/bin/msfconsole

4. Install Dependencies

   This project requires certain Python packages to run. Install them by running the following command:

  poetry shell
  poetry install  

5. Then Run the project:

  poetry run python main.py

6. (Optional) build the image and container:

  docker build -t aironman/aironmangpt:0.0.1 .

7. (Optional) run container:

  docker run -it --env-file .env aironman/aironmangpt:0.0.1

8. (Optional) run container:

  docker-compose run aironmangpt

9. (Optional, from scratch) use makefile:

  make all

10. (Optional, from scratch) build and run the container:

  make container-build container-run

DeHashed API Key (Optional, not tested yet)

1. # or Log In: Visit the DeHashed website (https://www.dehashed.com/). If you don't already have an account, you'll need to #. If you do, just log in.
2. Subscription: DeHashed is a paid service, so you'll need to subscribe to one of their plans to get access to the API. Choose a plan that fits your needs and complete the subscription process.
3. Accessing the API Key: Once you've subscribed, you can usually find your API key in your account settings or dashboard. Look for a section labeled "API" or something similar. If you're having trouble finding it, DeHashed's support or documentation might be able to help.
4. Security: Keep your API key secure. Don't share it with others or expose it in public code repositories.

OpenAI API Key

1. # or Log In: Go to the OpenAI website (https://openai.com/). You'll need to create an account if you don't have one, or log in if you do.

2. Getting the API Key: Once you have been granted access, you can find your API key in your OpenAI account dashboard. There should be a section for API keys or developer settings (https://platform.openai.com/api-keys).

3. Usage and Billing: Be aware of OpenAI's usage and billing policies. Depending on the volume of your requests and the specific models you use, you might incur charges. Plan accordingly and monitor your usage.

4. Security: As with any API key, it's crucial to keep your OpenAI key secure. Do not share it publicly or with anyone who should not have access to it.

5. General Tips for Managing API Keys: Environment Variables: Store your API keys in environment variables rather than hard-coding them into your project. This makes your application more secure and flexible.

6. .gitignore: If you're using Git, ensure your .env file or any file containing API keys is listed in your .gitignore file to prevent it from being uploaded to a public repository.

7. Documentation: Always refer to the official documentation of the API provider for the most accurate and up-to-date information on obtaining and using API keys.

8. By following these steps and guidelines, you'll be able to obtain the necessary API keys to integrate DeHashed and OpenAI services into your projects.

GPT4All (Optional, in progress)

The idea is to be able to use autonomous agents for each of the phases of a pentesting operation. To do this, an LLM will be used that interacts with each of the invocations of said agents and collects the inputs and outputs in a coherent and appropriate manner. To do this, I will try to use GPT4All to use some LLM that is not restricted by its original authors so that I can use these tools without restrictions.