Abstract-Advances in deep learning algorithms overshadow their security risk in software implementations. This paper discloses a set of vulnerabilities in popular deep learning frameworks including Caffe, TensorFlow, and Torch. Contrary to the small code size of deep learning models, these deep learning frameworks are complex, and they heavily depend 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 classification. 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 lead to either system compromise or recognition evasions. The goal of this paper is to draw attention to software implementations and call for community collaborative effort to improve security of deep learning frameworks.
Xiao et al. REF investigate the vulnerabilities (e.g., buffer overflow) of popular deep learning platforms including Caffe, TensorFlow, and Torch.
Security Risks in Deep Learning Implementations
2018 IEEE Security and Privacy Workshops (SPW)
2018 IEEE Security and Privacy Workshops (SPW)
Computer Science