  Advance in deep learning algorithms overshadows their security risk in
software implementations. This paper discloses a set of vulnerabilities in
popular deep learning frameworks including Caffe, TensorFlow, and Torch.
Contrast to the small code size of deep learning models, these deep learning
frameworks are complex and contain heavy dependencies on numerous open source
packages. This paper considers the risks caused by these vulnerabilities by
studying their impact on common deep learning applications such as voice
recognition and image classifications. By exploiting these framework
implementations, attackers can launch denial-of-service attacks that crash or
hang a deep learning application, or control-flow hijacking attacks that cause
either system compromise or recognition evasions. The goal of this paper is to
draw attention on the software implementations and call for the community
effort to improve the security of deep learning frameworks.
