Kuna: Decompiler Development in the Age of Coding Agents

Yeah, fair question.

I read this less as “build a decompiler with an LLM instead of training one to decompile,” and more as a different loop: you still get an artifact you can measure and improve against IDA / Ghidra / angr. Training on infinite compiler data is powerful for the first path; this post is mostly about the second one (i think, but i could wrong).

LLMs are famously inefficient, expensive and unreliable.

Creator here! Since I saw some questions below, I'd like to clarify some things.

Kuna is originally Ghidra ported to Rust, but it has changed quite a bit since. The results shown in the screenshot on the site are not a feature in Ghidra, which includes the Switch layout and some of the variable simplification. It's from Ghidra, previously talked about here [1].

> It would be cool to see agentic interpretation of function and variable names.

On DecBench, which I also created last week, you can see just that for Codex and Claude when given only assembly [2]. They dominate, but also have some significant recall problems.

> Why not train an LLM to be a decompiler instead of make one

Over the last two decades, this has been attempted _many_ times [3]. To date, there has been no successful project/paper that can compete with traditional decompilers on fundamental metrics. I speculate this is due to their inability to abstract the many-to-one mapping of optimization to code. This can change, but I desired to take a different approach.

If frontier labs continue to get better, they will become the de facto decompilation users. Why not get LLMs to help LLMs? Design a core decompiler that helps them more than others :).

[1]: https://www.youtube.com/watch?v=VP29biKLoSw

[2]: https://decbench.com/leaderboard/?dataset=sample-set

[3]: https://decompilation.wiki/fundamentals/neural-decompilation...

idalib with Claude Code already works really well. But honestly, despite what people have been saying, LLMs have been very good at decompiling for at least two years now, I have been using it for that purpose regularly. Even just copying disassembly from the current function and all nested called functions from IDA into ChatGPT is already unexpectedly good.

Interesting. A lot of data shows IDA Pro is significantly better than Ghidra: https://decbench.com/

Based on today's results, IDA Pro is ahead by 15 percentage points, which would mean, statistically, IDA Pro will recover perfect source code for 15% more functions than Ghidra on average.