  Many households include children who use voice personal assistants (VPA) such
as Amazon Alexa. Children benefit from the rich functionalities of VPAs and
third-party apps but are also exposed to new risks in the VPA ecosystem (e.g.,
inappropriate content or information collection). To study the risks VPAs pose
to children, we build a Natural Language Processing (NLP)-based system to
automatically interact with VPA apps and analyze the resulting conversations to
identify contents risky to children. We identify 28 child-directed apps with
risky contents and maintain a growing dataset of 31,966 non-overlapping app
behaviors collected from 3,434 Alexa apps. Our findings suggest that although
voice apps designed for children are subject to more policy requirements and
intensive vetting, children are still vulnerable to risky content. We then
conduct a user study showing that parents are more concerned about VPA apps
with inappropriate content than those that ask for personal information, but
many parents are not aware that risky apps of either type exist. Finally, we
identify a new threat to users of VPA apps: confounding utterances, or voice
commands shared by multiple apps that may cause a user to invoke or interact
with a different app than intended. We identify 4,487 confounding utterances,
including 581 shared by child-directed and non-child-directed apps.
