Has this idea been tested on models where the prompt is openly available? If so, how close to the original prompt is it? Is it just based on the idea that LLMs are good about repeating sections of their context? Or that LLMs know what a "prompt" is from the training corpus containing descriptions of LLMs, and can infer that their context contains their prompt?
I have a few reasons for assuming that these are normally accurate:
1. Different people using different tricks are able to uncover the same system prompts.
2. LLMs are really, really good at repeating text they have just seen.
3. To date, I have not seen a single example of a "hallucinated" system prompt that's caught people out.
You have to know the tricks - things like getting it to output a section at a time - but those tricks are pretty well established by now.
It doesn't need to be hallucinated to be a false system prompt.
I've seen plenty of examples of leaked system prompts that included instructions not to reveal the prompt, dating all the way back to Microsoft Bing! https://simonwillison.net/2023/Feb/9/sidney/
For the crowd that thinks it is possible:
Why can’t they just have a final non-LLM processing tool that looks for a specific string and never lets it through. That could include all of the tips and tricks for getting the LLM to encode and decode it. It may not ever be truly 100%, but I have to imagine it can get close enough that people think they have cracked it.
A service is not a model though and could maybe use inference techniques rather than just promoting.
Do they? I don’t think such expectation exists. Usually if you try to do it you need multiple attempts and you might only get it in pieces and with some variance.