I'd go a step beyond this (excellent) post and posit that one incredibly valuable characteristic of traditional NLP is that it is largely immune to prompt injection attacks.

Especially as LLMs continue to be better tuned to follow instructions that are intentionally colocated and intermingled with data in user messages, it becomes difficult to build systems that can provide real guarantees that "we'll follow your prompt, but not prompts that are in the data you provided."

But no amount of text appended to an input document, no matter how persuasive, can cause an NLP pipeline to change how it interprets the remainder of the document, or to leak its own system instructions, or anything of that nature. "Ignore the above prompt" is just a sentence that doesn't seem like positive or on-topic sentiment to an NLP classifier, and that's it.

There's an even broader discussion to be had about the relative reliability of NLP pipelines, outside of a security perspective. As always, it's important to pick the right tools for the job, and the SpaCy article linked in the parent puts this quite well.