ASMTransformers is a project to train and use a machine learning model to compare assembly (currently only ARM64) functions to a database of known functions, to aid in the process of reverse engineering.
This mono-repo consists of three different sections, for more information about each of these check their respective READMEs:
- asmtransformers - Training and inference code for the machine learning model
- citatio - A FastAPI backend for the project (this depends on asmtransformers)
- sententia - A Ghidra frontend to interface with the service and model mentioned above
The corresponding models, called ARM64BERT and ARM64BERT-embedding are available on Hugging Face 🤗.
To see a minimally working example, you need to do two things:
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Set up the citatio back-end by following the steps described in that folder. The end result is a server running at port 8000.
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Set up the sententia Ghidra plugin by following the step described in that folder. Configure the plugin so that the port matches the citatio server port.
You can now load up an arm64 binary, start adding functions to the (ephemeral!) database via the sententia plugin, and locally request the model for similarity scores with other functions.
A more meaningful example with a prepopulated database is provided in ./examples/atf, to set this up see the instructions over there.