Re: Is it a bug?
No, you have to go back, create test cases for every imaginable scenario, ...
Sorry, no. You seem to have a total misconception as to how machine-learning in general, and "deep-learning" (a.k.a. multi-layer, usually feed-forward) networks in particular, function. You seem to have latched onto the bogus idea that a machine learning algorithm needs to have "seen" every conceivable input it might encounter in order to be able to classify it.
In reality, the entire point of machine-learning algorithms is to be able to generalise to inputs it hasn't encountered in training. The art (and it's not quite a science, although some aspects of the process are well-understood) of good machine-learning design is to tread the line between poor generalisation (a.k.a. "overfitting" the training data) and poor classification ability (a.k.a. "underfitting" the training data).
It's a hard problem - and while the more (and more varied) the training data and time/computing resources available, the better performance you can expect, I'd be the last person to claim that deep-learning is going to crack general AI. Far from it. But it can be rather good at domain-specific problems, and as such I suspect will become a useful building-block of more sophisticated and multi-faceted systems of the future.
After all, a rather striking (if comparatively minor) and highly domain-specific aspect of human intelligence is our astounding facial recognition abilities. But then we have the benefit of millions of years of evolutionary "algorithm design" behind those abilities.