LLMs are good at producing what they/the public know.

In this case:

  LLMs know the USB Spec very well.

  LLMs know how to read raw packet dumps.

  LLMs know how to convert a packet dump to USB spec

  LLMs know how to write code to generate USB packets from the spec.

Basically, If you have a well-documented problem, the LLM is a shortcut to learning it yourself. LLMs fail when you have a novel or poorly documented problem. They also fail when you provide the LLM with terrible context or too much context.

IMO it's because they don't get burnt out by a lack of results.

After 5-6 consecutive approaches fail, I need a reason to think the next one might work out to stay motivated.

Claude will keep burning credits trying new approaches until something sticks. That's a huge advantage in a field where most of the things you try don't go anywhere.

LLMs seem to be trained to work very well against a goal, especially one it can verify against. I guess because it can easily know if it passed or failed, va other tasks where good/bad output is subjective

It's LLMs' generalist skills. I think reverse engineering is usually hard when you're in an unfamiliar domain. Eg I've never programmed a videogame or windows application but I'm trying to crack one. On the other hand if you know the domain, and know what the programmers' intentions must be in any given block of code, it's often straightforward. There are no unfamiliar domains for LLMs, including it seems proprietary software.

To some degree. But esp for drivers, you still need to know when they go wrong, and steer them right, or your code will either just not work or be an unmaintainable, not-upstreamable mess.

I don't think they're any more skilled at it than someone who knows how to reverse engineer stuff... But it is definitely a place where AI is amazing because reverse engineering is usually extremely time consuming and tedious. AI doesn't care about that.

It also has the benefit that it doesn't usually matter too much if it gets minor details wrong. It's definitely one of the areas - like hacking - where it's a) tedious and b) insensitive to mistakes where AI absolutely shines.