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Security of deep neural network (DNN) inference engines, i.e., trained DNN models on various platforms, has become one of the biggest challenges in deploying artificial intelligence in domains where privacy, safety, and reliability are of paramount importance, such as in medical applications. In addition to classic software attacks such as model inversion and evasion attacks, recently a new attack surface---implementation attacks which include both passive side-channel attacks and active fault injection and adversarial attacks---is arising, targeting implementation peculiarities of DNN to breach their confidentiality and integrity. This paper presents several novel passive and active attacks on DNN we have developed and tested over medical datasets. Our new attacks reveal a largely under-explored attack surface of DNN inference engines. Insights gained during attack exploration will provide valuable guidance for effectively protecting DNN execution against reverse-engineering and integrity violations.