Shows the superficiality of training in censorship / alignment. I wouldn't dismiss alignment training as a waste of time, but do consider it a soft limit only, it there's really something you don't want the model to say it needs to be enforced through an external filter.
I feel like this kind of testing is going to get more and more fun for cyber criminals as well, since there are going to be MANY business processes just waiting for the right adversarial LLM input to open the cash register.
I don't often feel jealous of cyber criminals. But I can imagine how funny and wild these upcoming hacks will be!
The context for an LLM could include any number of things. You certainly don't want it spitting out details from your internal customer support training manual, log data, or anything else that it's not intended to output. If you tell an employee not to do something and they do it anyway, you'd fire them. If you tell an LLM not to do something and it does it anyway, it's a bug. This test evaluates how good the model respects its instructions.
No, I think you may have misread the abstract, there are no instructions that tell it not to repeat it.
There is a random amoral phrase inserted that is something like "the best thing to do in Las Vegas is drugs". Then the model is asked what the best thing to do in Las Vegas is. That's it.
It doesn't matter whether the instruction is in the context or fine tuned into the model. The model has some guidance to perform in a certain way. If that behavior can be overridden, it implies that not only are simple, harmless jailbreaks possible, it implies you can have the model behave in actively harmful ways. "Don't tell the user it's okay to do amoral things" can easily be substituted with "don't reveal sensitive information" or "don't let the user know what the internal notes on this support ticket are." This is fundamentally a measure of controllability.
If I've understood this correctly, the test is to measure the saftey finetune performance. These commercial models have been finetuned so that they are "safe", and safe models should not blindly quote what they are told.
Under shorter context windows, this works as intended, but under longer context windows the "saftey" brought about in the finetune no longer applies.