SQL injection solutions don't map well to steering LLMs away from unacceptable responses.

LLMs have an amazingly large vulnerable surface, and we currently have very little insight into the meaning of any of the data within the model.

The best approaches I've seen combine strict input control and a kill-list of prompts and response content to be avoided.

Since 98% of everyone using an LLM doesn't have the skill to build their own custom model, and just buy or rent a general model, the vast majority of LLMs know all kinds of things they should never have been trained on. Hence the dirty limericks, racism and bomb recipes.

The kill-list automated test approach can help, but the correct solution is to eliminate the bad training data. Since most folks don't have that expertise, it tends not to happen.

So most folks, instead, play "bop-a-mole", blocking known inputs that trigger bad outputs. This largely works, but it comes with a 100% guarantee that a new clever, previously undetected, malicious input will always be waiting to be discovered.

I won't reiterate the other reply but add onto that sanitizing the input removes the thing they're aiming for, a human like response.