LLM4Decompile: Decompiling Binary Code with LLM

• rizin

  46 2,436 9.8 C

  UNIX-like reverse engineering framework and command-line toolset.

  

• LLM4Decompile

  2 2,415 8.7 Python

  Reverse Engineering: Decompiling Binary Code with Large Language Models

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• deepcompyle

  1 0 6.1 Python

  Pretraining transformers to decompile Python bytecodes

Hey, I am working on my own LLM-based decompiler for Python bytecode (https://github.com/kukas/deepcompyle). I feel there are not many people working on this research direction but I think it could be quite interesting, especially now that longer attention contexts are becoming feasible. If anyone knows a team that is working on this, I would be quite interested in cooperation.

• ghidra_tools

  6 281 5.4 Python

  A collection of Ghidra scripts, including the GPT-3 powered code analyser and annotator, G-3PO.

  

relevant: https://news.ycombinator.com/item?id=34250872 (G-3PO: A protocol droid for Ghidra, or GPT-3 for reverse-engineering <https://github.com/tenable/ghidra_tools/blob/main/g3po/g3po....>; Jan, 2023; 44 comments)

• aici

  6 1,714 9.9 Rust

  AICI: Prompts as (Wasm) Programs

  

I have been planning to work on something like this. I think that eventually, someone will crack the "binary in -> good source code out of LLM" pipeline but we are probably a few years away from that still. I say a few years because I don't think there's a huge pile of money sitting at the end of this problem, but maybe I'm wrong.

A really good "stop-gap" approach would be to build a decompilation pipeline using Ghidra in headless mode and then combine the strict syntax correctness of a decompiler with the "intuition/system 1 skills" of an LLM. My inspiration for this setup comes from two recent advancements, both shared here on HN:

1. AlphaGeometry: The Decompiler and the LLM should complement each other, covering each other's weaknesses. https://deepmind.google/discover/blog/alphageometry-an-olymp...

2. AICI: We need a better way of "hacking" on top of these models, and being able to use something like AICI as the "glue" to coordinate the generation of C source. I don't really want the weights of my LLM to be used to generate syntactically correct C source, I want the LLM to think in terms of variable names, "snippet patterns" and architectural choices while other tools (Ghidra, LLVM) worry about the rest. https://github.com/microsoft/aici

Obviously this is all hand-wavey armchair commentary from a former grad student who just thinks this stuff is cool. Huge props to these researchers for diving into this. I know the authors already mentioned incorporating Ghidra into their future work, so I know they're on the right track.

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