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Two-Factor Authentication (2FA) hardens an organization against user account compromise, but adds an extra step to organizations’ mission-critical tasks. We investigate to what extent quantitative analysis of operational logs of 2FA systems both supports and challenges recent results from user studies and surveys identifying usability challenges in 2FA systems. Using tens of millions of logs and records kept at two public universities, we quantify the at-scale impact on organizations and their employees during a mandatory 2FA implementation. We show the multiplicative effects of device remembrance, fragmented login services, and authentication timeouts on user burden. We find that user burden does not deviate far from other compliance and risk management time requirements already common to large organizations. We investigate the cause of more than one in twenty 2FA ceremonies being aborted or failing, and the variance in user experience across users. We hope our analysis will empower more organizations to protect themselves with 2FA. 

The proliferation of the Internet of Things has increased reliance on voice-controlled devices to perform everyday tasks. Although these devices rely on accurate speech recognition for correct functionality, many users experience frequent misinterpretations in normal use. In this work, we conduct an empirical analysis of interpretation errors made by Amazon Alexa, the speech-recognition engine that powers the Amazon Echo family of devices. We leverage a dataset of 11,460 speech samples containing English words spoken by American speakers and identify where Alexa misinterprets the audio inputs, how often, and why. We find that certain misinterpretations appear consistently in repeated trials and are systematic. Next, we present and validate a new attack, called skill squatting. In skill squatting, an attacker leverages systematic errors to route a user to malicious application without their knowledge. In a variant of the attack we call spear skill squatting, we further demonstrate that this attack can be targeted at specific demographic groups. We conclude with a discussion of the security implications of speech interpretation errors, countermeasures, and future work.