There is no way to determine that a non-trivial neural network won't drastically diverge in output due to small changes in input (eg one pixel attacks on image classifiers). This is true for all current models I know of.

Almost all neural network implementations have continuous outputs (ie the nodes in the output layer produce a value between 0 and 1). That doesn't change the above issue at all.

This is much less of an issue with traditional methods