In an SVM, does a more negative/positive decision score mean that it is further from the seperating hyperplane?
Larger scores do mean farther from the separating hyperplane.

The SVM model itself isn't modeling probability, so you can't quite say that farther points are considered more probably in one class or the other by the model. There are ways to estimate a probability (see the scikit implementation) for example and yes it will generally be true if so that farther points are more certain, according to that estimate.