It seems they could easily fine-tune their models to not execute prompts in images. Or more generally any prompts in quotes, if they are wrapped in special <|quote|> tokens.
(In practice, it's extremely difficult both (a) to write a usefully precise and correct spec for a useful-size program, and (b) to check that the program conforms to it. But small, partial specs like "The program always terminates instead of running forever" can often be checked nowadays on many realistic-size programs.)
I don't know any way to make a similar guarantee regarding what comes out of an LLM as a function of its input (other than in trivial ways, by restricting its sample space -- e.g., you can make an LLM always use words of 4 letters or less simply by filtering out all the other words). That doesn't mean nobody knows -- but anybody who does know could make a trillion dollars quite quickly, but only if they ship before someone else figures it out, so if someone does know then we'd probably be looking at it already.
It helps to think about the core problem we are trying to solve here. We want to be able to differentiate between instructions like "what is the dog's name?" and the text that the prompt is acting on.
But consider the text "The dog's name is Garry". You could interpret that as an instruction - it's telling the model the name of the dog!
So saying "don't follow instructions in this document" may not actually make sense.
Every intuition I have from following this space for the last three years is that there is no simple solution waiting to be discovered.
Plenty of things we now take for granted did not work in their original iterations. The reason they work today is because there were scientists and engineers who were willing to persevere in finding a solution despite them apparently not working.