I do have a hunch in that we might be able to utilize tiny LLMs to figure out parts that might possibly be brittle and combine them with traditional generation/mutation-based fuzzing to generate fuzz targets that are more likely to trigger an edge case.

I did not think of applying LLMs on fuzzing at all until I saw llvm-hackme[0] which does both traditional mutation fuzzing as well as LLM-generated targeted regression test cases, where the LLM is pretty effective in understanding the PR and targeting edge cases! It was pretty impressive and I keep getting to think on how we can actually combine LLMs to make fuzzing much more efficient & effective.

// Sorry about the yet-another-LLM-comment. I really love PLs and I'm terribly sorry that I'm contributing yet another LLM-related content (instead of the more interesting stuff!)

[0]: https://github.com/dtcxzyw/llvm-hackme

I love good programming languages, if that counts for anything, and I think there's a huge amount of room and value in "using an LLM to translate one programming language to another".

I also think your instincts are on a useful path. Perhaps using a very small and stupid LLM to generate plausible-sounding-but-probably-wrong programs might be a path to generating interesting test cases?