Yes, LLMs are prone to semantic consistency traps. I see a version of this in a system I have that transcribes a lot of noisy low quality audio (radio comms).

Sometimes it works out, in that an unrecognizable word or two is replaced with reasonable assumptions based on the semantics established by the words that came before the signal degraded.

But the bad audio might also result in words that don't align well to what came before, or represent alternate (mis)interpretations. Now this is part of the context and the next several tokens align to this new path regardless of what is said in the audio.

In pre-LLM transcription, you might get a nonsense word or two when the audio transiently degrades, but the specific meaning of the nonsense words doesn't influence the transcription of audio following the degradation.