[thfuran]

>If you decompile the binary to source, then compile the source back to binary you should get the original binary.

You really can't expect that if you're not using exactly the same version of exactly the same compiler with exactly the same flags, and often not even then.

[moyix]

Yes, that's a limitation of trying to ensure exact binary reconstruction. Luckily there is also a separate line of work on detecting the compiler version and optimization flags based on a binary – it turns out this is not that hard and it's easy to get a bunch of labeled data for a classifier.

If folks are interested in reading more there's a nice paper by Grammatech on the idea: https://eschulte.github.io/data/bed.pdf (though it's pre-LLM and uses evolutionary algorithms on the initial decompilation to search for a version that recompiles exactly).

[vasvir]

Right.

A less formidable problem with higher chances of succeeding is from a given binary to figure out first compiler, compiler-version, compiler-flags.

From there you could have a model for every combination or at least a model for the compiler variant and use the other info (version, flags) as input to the model.

[dheera]

Maybe we then need an LLM to tell us if two pieces of compiled code are equivalent in an input-output mapping sense (ignoring execution time).

I'm actually serious; it would be exceedingly easy to get training data for this just by running the same source code through a bunch of different compiler versions and optimization flags.

[saagarjha]

An LLM cannot do this. I don’t even mean this in a formal sense, because your problem is addressed by Rice’s Theorem, which places bounds on what any system (LLM or not) can do here; I mean it in the sense that an LLM isn’t even appropriate to use here because the best it can possibly do is provide you with its best guess at the answer. And while this might be a useful property for decompilation in general that’s not what was being discussed here.

[dheera]

Rice's theorem does NOT prevent a program from giving correct answers to non-trivial properties of programs (including the halting problem or other undecidable problems) for 99.99% of inputs and "I don't know" for 0.01% of inputs. It only states that you cannot write a program that provides a correct and definitive yes-or-no for 100% of inputs.

For a decompiler, being able to decompile even 90% of programs would be awesome. We're not looking for theoretical perfectness.

[thfuran]

Why would an llm be the tool for that job?

[dheera]

Without analytical thinking how else would you come to conviction that two functions are identical, for a computationally unfeasible number of possible inputs?

[kolinko]

Formal logic / formal proofs. We have good systems for verifying that.

The proper flow is that you use LLM to generate decompilation steps, along with potential proofs, and then use old algorithms from 1970s that verify that the steps are correct.

Source: I built a decompiler for EVM, arguably the best one on the market, and to some extent it was how it worked (and others comparable in class).

The issue was always the exploration of possible transformations of code, once you manage to find the right ones (which LLMs can propose way better than old hard coded rules and SMT solvers), it's simple to verify that the transformations are correct.

[Eager]

You try your best, and if you provide enough examples, it will undoubtedly get figured out.

[lolinder]

I think you're misunderstanding OP's objection. It's not simply a matter of going back and forth with the LLM until eventually (infinite monkeys on typewriters style) it gets the same binary as before: Even if you got the exact same source code as the original there's still no automated way to tell that you're done because the bits you get back out of the recompile step will almost certainly not be the same, even if your decompiled source were identical in every way. They might even vary quite substantially depending on a lot of different environmental factors.

Reproducible builds are hard to pull off cooperatively, when you control the pipeline that built the original binary and can work to eliminate all sources of variation. It's simply not going to happen in a decompiler like this.

[blagie]

Well, no, but yes.

The critical piece is that this can be done in training. If I collect a large number of C programs from github, compile them (in a deterministic fashion), I can use that as a training, test, and validation set. The output of the ML ought to compile to the same way given the same environment.

Indeed, I can train over multiple deterministic build environments (e.g. different compilers, different compiler flags) to be even more robust.

The second critical piece is that for something like a GAN, it doesn't need to be identical. You have two ML algorithms competing:

- One is trying to identify generated versus ground-truth source code

- One is trying to generate source code

Virtually all ML tasks are trained this way, and it doesn't matter. I have images and descriptions, and all the ML needs to do is generate an indistinguishable description.

So if I give the poster a lot more benefit of the doubt on what they wanted to say, it can make sense.

[lolinder]

Oh, I was assuming that Eager was responding to klik99's question about how we could identify hallucinations in the output—round tripping doesn't help with that.

If what they're actually saying is that it's possible to train a model to low loss and then you just have to trust the results, yes, what you say makes sense.

[blagie]

I haven't found many places where I trust the results of an ML algorithm. I've found many places where they work astonishingly well 30-95% of the time, which is to say, save me or others a bunch of time.

It's been years, but I'm thinking back through things I've reverse-engineered before, and having something which kinda works most of the time would be super-useful still as a starting point.

[incrudible]

Have you ever trained a GAN?

[blagie]

Technically, yes!

A more reasonable answer, though, is "no."

I've technically gone through random tutorials and trained various toy networks, including a GAN at some point, but I don't think that should really count. I also have a ton of experience with neural networks that's decades out-of-date (HUNDREDS of nodes, doing things like OCR). And I've read a bunch of modern papers and used a bunch of Hugging Face models.

Which is to say, I'm not completely ignorant, but I do not have credible experience training GANs.

[weinzierl]

That's true but a solvable problem. I once tried to reproduce the build of an uncooperative party and it was mainly tedious and boring.

The space of possible compiler arguments is huge, but ultimately what is actually used is mostly on a small surface.

Apart from that, I wrote a small tool to normalize the version string, timestamps and file path' in the binaries before I compared them. I know there are other sources of non-determinism, but these three things were enough in my case.

The hardest part were the numerous file path' from the build machine. I had not expected that. In hindsight, stripping both binaries before comparison might have helped, but I don't remember why I didn't do that.

[junon]

Err, no, sorry, it won't. Compilers don't work that way. There's a lot of ways to compile down source to machine code and the output changes from compiler version to compiler version. The LLM would have to know exactly how the compiler worked at which version to do this. So the idea is technically possible but not technically feasible.

[thfuran]

What exactly are you suggesting will get figured out?

[spqrr]

The mapping from binary to source code.

[thfuran]

Even ignoring all sources of irreproducibility, there does not exist a bijection between source and binary artifact irrespective of tool chain. Two different toolchains could compile the same source to different binaries or different sources to the same binary. And you absolutely shouldn't be ignoring sources of irreproducibility in this context, since they'll cause even the same toolchain to keep producing different binaries given the same source.

[achrono]

Exactly, but neither the source nor the binary is what's truly important here. The real question is: can the LLM generate the functionally valid source equivalent of the binary at hand? If I disassemble Microsoft Paint, can I get code that will result in a mostly functional version of Microsoft Paint, or will I just get 515 compile errors instead?

[Brian_K_White]

This is what I thought the question was really about.

I assume that an llm will simply see patterns that look similar to other patterns and make assosciations and assume ewuivalences on that level, meanwhile real code is full of things where the programmer, especially assembly programmers, modify something by a single instruction or offset value etc to get a very specific and functionally important result.

Often the result is code that not only isn't obvious, it's nominaly flatly wrong, violating standards, specs, intended function, datasheet docs, etc. If all you knew were the rules written in the docs, the code is broken and invalid.

Is the llm really going to see or understand the intent of that?

They find matching patterns in other existing stuff, and to the user who can not see the infinite body of that other stuff the llm pulled from, it looks like the llm understood the intent of a question, but I say it just found the prior work of some human who understood a similar intent somewhere else.

Maybe an llm or some other flavor of ai can operate some other way like actually playing out the binary like executing in a debugger and map out the results not just look at the code as fuzzy matching patterns. Can that take the place of understanding the intents the way a human would reading the decompiled assembly?

Guess we'll be finding out sooner of later since of course it will all be tried.

[layer8]

The question was about the reverse mapping.

[fao_]

Except LLMs cannot reason.

[LoganDark]

LLMs can mimic past examples of reasoning from the dataset. So, it can re-use reasoning that it has already been trained on. If the network manages to generalize well enough across its training data, then it can get close to reproducing general reasoning. But it can't yet fully get there, of course.

[mrtesthah]

Do you have evidence LLMs can indeed generalize outside their training data distribution?

https://twitter.com/abacaj/status/1721223737729581437/photo/...

[LoganDark]

No. I know only that they can generalize within it, and only to a limited degree, but don't have solid evidence of even that.

[fao_]

So what you're saying is there's tenuous-at-best, non-"solid" evidence that LLMs can reason even within their training data.

And yet I'm currently sitting at -1 for stating the blisteringly obvious. Lmao