Awesome
aiDAPal
aiDAPal is an IDA Pro plugin that uses a locally running LLM that has been fine-tuned for Hex-Rays pseudocode to assist with code analysis. Further details about this project can be found on our blog: https://atredis.com/blog/2024/6/3/how-to-train-your-large-language-model
This repository contains the IDA Pro plugin, the associated fine-tuned weights as well as training dataset can be downloaded from Hugging Face: https://huggingface.co/AverageBusinessUser/aidapal
Requirements / Setup
LLM Hosting/Service
This plugin uses Ollama's API for accessing the associated LLM - https://ollama.com/
Download the fine-tuned weights and Ollama modelfile:
- Model https://huggingface.co/AverageBusinessUser/aidapal/blob/main/aidapal-8k.Q4_K_M.gguf
- Ollama Modelfile: https://huggingface.co/AverageBusinessUser/aidapal/blob/main/aidapal.modelfile
Configure Ollama by running the following within the directory you downloaded the weights and modelfile:
ollama create aidapal -f aidapal.modelfile
Running ollama list should show an entry for aidapal after this step:
% ollama list
NAME ID SIZE MODIFIED
aidapal:latest d04d7b95027b 4.4 GB 2 months ago
Python requirements
The plugin uses requests for HTTP requests to the API server - https://requests.readthedocs.io/en/latest/user/install/#install
By default the plugin uses the Ollama service running on the local host and is configured to use the aidapal weights, edit the plugin if either of these values should be different:
# this list holds the list of models registered with Ollama to be accessible from the plugin.
models = ['aidapal']
# where ollama service is running
ollama_url = "http://localhost:11434/api/generate"
Usage
After loading the plugin, a new context menu is accessible from the Hex-Rays window that will initiate analysis:
When generation is complete, a results dialog will pop up, allowing you to accept or reject the results:
Once accepted, the Hex-Rays output will be updated with your changes. A full example of this can be seen in the following screengrab:
The speed of generation is going to depend on the hardware you are executing on, the above example is representative of usage on ARM (Mx) based macbooks.