Comment by webdevver | Hacker News Reader

Comment by webdevver | Hacker News Reader

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i have no idea why nobody is doing it - it is such an obvious use case of LLMs. i guess the reveng market is much smaller than most people realized?

then again, who needs reveng when you can use said LLMs to write new software "just in time" with the same API.

reveng also was one of those industries that always had a very suspicious crowd of people - i dont mean malicious, i mean... a lot of them drew a disturbing amount of pleasure from doing incredibly labourious work, sort of like someone who enjoys putting together an airfix model over many months with a microscopic brush and tweezers.

so i wonder if a lot of them perversely enjoy starting at reams of bytes and putting together this 10,000 piece puzzle, and having an llm solve it for them is a deep affront to their tastes.

Is it really an obvious use case of LLMs? Traditional byte code to source decompilers are faster, use less memory, and are deterministic. Using a LLM to decompile code makes as much sense as using a LLM to compile code.

That said there are probably ways a LLM could improve a decompiler in a way that does not impact its correctness. Like deriving class and variables names based on context, when symbols are missing or obfuscated.

... until you realize that the LLM-generated code doesn't even compile, or you need a PhD to write all the prompts needed to have a prototype instead of the real thing.

it doesnt have to be traditional chat interface style.

why doens't someone train an LLM on predicting source code given a sequence of input machine code tokens? that is so obvious. why does nobody do it?

Not worth the effort

youre probably right... for the public market, anyway. blackhat state actors would probably not mind having something like that. but they'd never talk about it in public.

or maybe its not even neccesary, and doing something akin to fuzzing syscalls 'but smartly' probably yields more results.

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