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.
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Hey Alexa, is this Skill Safe?: Taking a Closer Look at the Alexa Skill Ecosystem
Amazon's voice-based assistant, Alexa, enables users to directly interact with various web services through natural language dialogues. It provides developers with the option to create third-party applications (known as Skills) to run on top of Alexa. While such applications ease users' interaction with smart devices and bolster a number of additional services, they also raise security and privacy concerns due to the personal setting they operate in. This paper aims to perform a systematic analysis of the Alexa skill ecosystem. We perform the first large-scale analysis of Alexa skills, obtained from seven different skill stores totaling to 90,194 unique skills. Our analysis reveals several limitations that exist in the current skill vetting process. We show that not only can a malicious user publish a skill under any arbitrary developer/company name, but she can also make backend code changes after approval to coax users into revealing unwanted information.
We, next, formalize the different skill-squatting techniques and evaluate the efficacy of such techniques. We find that while certain approaches are more favorable than others, there is no substantial abuse of skill squatting in the real world.
Lastly, we study the prevalence of privacy policies across different categories of skill, and more importantly the policy content of skills that use the Alexa permission model to access sensitive user data. We find that around 23.3% of such skills do not fully disclose the data types associated with the permissions requested.
We conclude by providing some suggestions for strengthening the overall ecosystem, and thereby enhance transparency for end-users.
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- Award ID(s):
- 1849997
- NSF-PAR ID:
- 10216717
- Date Published:
- Journal Name:
- Network and Distributed Systems Security (NDSS) Symposium2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.14722/ndss.2021.23111
