But you can't trust any LLM, or any output from any LLM. You need to protect against stuff like this regardless. They could all do it, and quite accidentally. Even an LLM you trained yourself could do this to you, based on the training data and based on RAG.

But if you want to avoid the specific case we are talking about where your inference provider sees everything you do, and ignores your preferences, then running someone's LLM on your own inference is the only way forward.