The real world use cases for LLM poisoning is to attack places where those models are used via API on the backend, for data classification and fuzzy logic tasks (like a security incident prioritization in a SOC environment). There are no thumbs down buttons in the API and usually there's the opposite – promise of not using the customer data for training purposes.

The question was where should users draw the line? Producing gibberish text is extremely noticeable and therefore not really a useful poisoning attack instead the goal is something less noticeable.

Meanwhile essentially 100% of lengthy LLM responses contain errors, so reporting any error is essentially the same thing as doing nothing.