Attention is generally length invariant. You take some transformation on the hidden representations (/+ inputs) at that each time step, and then you normalize over all the transformed values to get weights that sum to one. No part of this is constrained by length.

For CNNs, any network that has pooling has the potential to be length/dimension invariant. Whether it actually is is a combination of the architectural design and an implementation detail (e.g. some implementations when trying to pool will specifically define a pooling operation over, say, a 9x9 window. You could define the same pooling operation over a variable-dimension window).

The length/dimension invariance aren't a special or novel property. In the case of attention it's built in. In the case of CNNs, the convolutions are not length invariant, but depending on the architecture, the pooling operations are (or can be modified to be).