When the belief is confidently wrong
The kidnapped robot problem describes a scenario where the robot's actual position suddenly and drastically diverges from its tracked belief, either literally, someone picks the robot up and sets it down somewhere else, or effectively, accumulated small errors finally compound into a tracking failure that snowballs. In both cases the danger isn't just that the robot is lost, it's that the localization system may still report a tight, confident belief around the wrong position, because nothing in the predict-update math automatically knows to doubt itself.
This is a serious practical problem because a confidently wrong belief is worse than an honestly uncertain one. A robot that knows it's uncertain can slow down, be cautious, or actively seek clarifying sensor data. A robot that's confidently wrong will keep acting on bad information, misinterpreting sensor mismatches as sensor noise rather than as evidence that its whole belief needs to be reconsidered.