Smart Home Personal Assistants (SPA) are an emerging innovation that is changing the means by which home users interact with technology. However, several elements expose these systems to various risks: (i) the open nature of the voice channel they use, (ii) the complexity of their architecture, (iii) the AI features they rely on, and (iv) their use of a wide range of underlying technologies. This article presents an in-depth review of SPA’s security and privacy issues, categorizing the most important attack vectors and their countermeasures. Based on this, we discuss open research challenges that can help steer the community to tackle and address current security and privacy issues in SPA. One of our key findings is that even though the attack surface of SPA is conspicuously broad and there has been a significant amount of recent research efforts in this area, research has so far focused on a small part of the attack surface, particularly on issues related to the interaction between the user and the SPA devices. To the best of our knowledge, this is the first article to conduct such a comprehensive review and characterization of the security and privacy issues and countermeasures of SPA.

Skills, are essential components in Smart Personal Assistants (SPA). The number of skills has grown rapidly, dominated by a changing environment that has no clear business model. Skills can access personal information and this may pose a risk to users. However, there is little information about how this ecosystem works, let alone the tools that can facilitate its study. In this paper, we present the largest systematic measurement of the Amazon Alexa skill ecosystem to date. We study developers' practices in this ecosystem, including how they collect and justify the need for sensitive information, by designing a methodology to identify over-privileged skills with broken privacy policies. We collect 199,295 Alexa skills and uncover that around 43% of the skills (and 50% of the developers) that request these permissions follow bad privacy practices, including (partially) broken data permissions traceability. In order to perform this kind of analysis at scale, we present SkillVet that leverages machine learning and natural language processing techniques, and generates high-accuracy prediction sets. We report several concerning practices, including how developers can bypass Alexa's permission system through account linking and conversational skills, and offer recommendations on how to improve transparency, privacy and security. Resulting from the responsible disclosure we did, 13% of the reported issues no longer pose a threat at submission time.

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The unprecedented adoption of messaging platforms for work and recreation has made it an attractive target for malicious actors. In this context, third-party apps (so-called chatbots) offer a variety of attractive functionalities that support the experience in large channels. Unfortunately, under the current permission and deployment models, chatbots in messaging systems could steal information from channels without the victim's awareness. In this paper, we propose a methodology that incorporates static and dynamic analysis for automatically assessing security and privacy issues in messaging platform chatbots. We also provide preliminary findings from the popular Discord platform that highlight the risks that chatbots pose to users. Unlike other popular platforms like Slack or MS Teams, Discord does not implement user-permission checks-a task entrusted to third-party developers. Among others, we find that 55% of chatbots from a leading Discord repository request the "administrator" permission, and only 4.35% of chatbots with permissions actually provide a privacy policy. CCS CONCEPTS• Security and privacy → Social network security and privacy; Web application security; Usability in security and privacy; Privacy protections.

This paper investigates the potential for spreading misinformation via third-party voice applications in voice assistant ecosystems such as Amazon Alexa and Google Assistant. Our work fills a gap in prior work on privacy issues associated with third-party voice applications, looking at security issues related to outputs from such applications rather than compromises to privacy from user inputs. We define misinformation in the context of third-party voice applications and implement an infrastructure for testing third-party voice applications using automated natural language interaction. Using our infrastructure, we identify -for the first time -several instances of misinformation in third-party voice applications currently available on the Google Assistant and Amazon Alexa platforms. We then discuss the implications of our work for developing measures to pre-empt the threat of misinformation and other types of harmful content in third-party voice assistants becoming more significant in the future. CCS CONCEPTS• Security and privacy → Human and societal aspects of security and privacy; • Human-centered computing → Human computer interaction (HCI).