If I'm not mistaken ROOM (ObjecTime, Rational Rose RealTime) was also heavily based on it. I worked in a company that developed real time software for printing machines with it and liked it a lot.
I think that's very difficult. To detect prompts you need to have natural language understand and therefore probably another detection LLM which is itself probably vunerable to prompt injection.
I also use niche questions a lot but mostly to check how much the models tend to hallucinate. E.g. I start asking about rank badges in Star Trek which they usually get right and then I ask about specific (non existing) rank badges shaped like strawberries or something like that. Or I ask about smaller German cities and what's famous about them.
I know without the ability to search it's very unlikely the model actually has accurate "memories" about these things, I just hope one day they will acutally know that their "memory" is bad or non-existing and they will tell me so instead of hallucinating something.
For me it's mostly about indentation / scope depth. So I prefer to have some early exits with precondition checks at the beginning, these are things I don't have to worry about afterwards and I can start with the rest at indentation level "0". The "real" result is at the end.