Answering end user security questions is challenging. While large language models (LLMs) like GPT, Llama, and Gemini are far from error-free, they have shown promise in answering a variety of questions outside of security. We qualitatively evaluated responses from three popular LLMs to 900 systematically collected security questions in the first such evaluation in the area of end user security. While LLMs demonstrate broad generalist “knowledge” of end user security information, there are patterns of errors and limitations across LLMs-including stale, inaccurate, and incomplete answers, as well as indirect or unresponsive communication styles-which negatively impact the user experience. Based on these patterns, we suggest directions for improved model development and recommend user strategies for interacting with LLMs when seeking assistance with security.

In this study, we explore the effectiveness of persuasive messages endorsing the adoption of a privacy protection technology (IoT Inspector) tailored to individuals’ regulatory focus (promotion or prevention). We explore if and how regulatory fit (i.e., tuning the goal-pursuit mechanism to individuals’ internal regulatory focus) can increase persuasion and adoption. We conducted a between-subject experiment (N = 236) presenting participants with the IoT Inspector in gain ("Privacy Enhancing Technology"—PET) or loss ("Privacy Preserving Technology"—PPT) framing. Results show that the effect of regulatory fit on adoption is mediated by trust and privacy calculus processes: prevention-focused users who read the PPT message trust the tool more. Furthermore, privacy calculus favors using the tool when promotion-focused individuals read the PET message. We discuss the contribution of understanding the cognitive mechanisms behind regulatory fit in privacy decision-making to support privacy protection.

Home Internet of Things (IoT) devices can be difficult for users to secure. Prior work has suggested measuring these devices' network behaviors and using these characterizations to create allowlists of permitted endpoints. Unfortunately, previous studies have typically been conducted in controlled lab settings, with one or two devices per product. In this paper, we examine whether popular home IoT products' network behaviors generalize via both in-lab experiments of 24 devices and a large, crowdsourced dataset of IoT devices in the wild. We find that observing traffic from one device in one lab is often insufficient to fully characterize an IoT product's network behaviors. For example, specifying which endpoints a device may contact based on initial measurements in our lab led 25% of products to stop functioning later, and even more when using a VPN. We then used the crowdsourced dataset to better understand this traffic's heterogeneity and pinpoint how to create more generalizable allowlists. We identified causes of failure, such as regionalization, CDN usage, third-party integrations, and API changes. Finally, we used the crowdsourced data in numerous configurations to specify which endpoints each product in our lab could contact. We found that domain-level allowlists enabled the majority of devices to function in our lab using data collected years in the past. For the remaining devices, we characterize how to mitigate the failures observed and pave the way to creating more generalizable allowlists.

Smart home devices are constantly exchanging data with a variety of remote endpoints. This data encompasses diverse information, from device operation and status to sensitive user information like behavioral usage patterns. However, there is a lack of transparency regarding where such data goes and with whom it is potentially shared. This paper investigates the diverse endpoints that smart home Internet-of-Things (IoT) devices contact to better understand and reason about the IoT backend infrastructure, thereby providing insights into potential data privacy risks. We analyze data from 5,413 users and 25,123 IoT devices using the IoT Inspector, an open-source application allowing users to monitor traffic from smart home devices on their networks. First, we develop semi-automated techniques to map remote endpoints to organizations and their business types to shed light on their potential relationships with IoT end products. We discover that IoT devices contact more third or support-party domains than first-party domains. We also see that the distribution of contacted endpoints varies based on the user's location and across vendors manufacturing similar functional devices, where some devices are more exposed to third parties than others. Our analysis also reveals the major organizations providing backend support for IoT smart devices and provides insights into the temporal evolution of cross-border data-sharing practices.

Increased use of technology in schools raises new privacy and security challenges for K-12 students—and harms such as commercialization of student data, exposure of student data in security breaches, and expanded tracking of students—but the extent of these challenges is unclear. In this paper, first, we interviewed 18 school officials and IT personnel to understand what educational technologies districts use and how they manage student privacy and security around these technologies. Second, to determine if these educational technologies are frequently endorsed across United States (US) public schools, we compiled a list of linked educational technology websites scraped from 15,573 K-12 public school/district domains and analyzed them for privacy risks. Our findings suggest that administrators lack resources to properly assess privacy and security issues around educational technologies even though they do pose potential privacy issues. Based on these findings, we make recommendations for policymakers, educators, and the CHI research community.

IoT devices are increasingly used in consumer homes. Despite recent works in characterizing IoT TLS usage for a limited number of in-lab devices, there exists a gap in quantitatively understanding TLS behaviors from devices in the wild and server-side certificate management.

The network communication between Internet of Things (IoT) devices on the same local network has significant implications for platform and device interoperability, security, privacy, and correctness. Yet, the analysis of local home Wi-Fi network traffic and its associated security and privacy threats have been largely ignored by prior literature, which typically focuses on studying the communication between IoT devices and cloud end-points, or detecting vulnerable IoT devices exposed to the Internet. In this paper, we present a comprehensive and empirical measurement study to shed light on the local communication within a smart home deployment and its threats. We use a unique combination of passive network traffic captures, protocol honeypots, dynamic mobile app analysis, and crowdsourced IoT data from participants to identify and analyze a wide range of device activities on the local network. We then analyze these datasets to characterize local network protocols, security and privacy threats associated with them. Our analysis reveals vulnerable devices, insecure use of network protocols, and sensitive data exposure by IoT devices. We provide evidence of how this information is exfiltrated to remote servers by mobile apps and third-party SDKs, potentially for household fingerprinting, surveillance and cross-device tracking. We make our datasets and analysis publicly available to support further research in this area.

The opaque data practices in smart home devices have raised significant privacy concerns for smart home users and bystanders. One way to learn about the data practices is through privacy-related notifications. However, how to deliver these notifications to users and bystanders and increase their awareness of data practices is not clear. We surveyed 136 users and 123 bystanders to understand their preferences of receiving privacy-related notifications in smart homes. We further collected their responses to four mechanisms that improve privacy awareness (e.g., Data Dashboard) as well as their selections of mechanisms in four different scenarios (e.g., friend visiting). Our results showed the pros and cons of each privacy awareness mechanism, e.g., Data Dashboard can help reduce bystanders’ dependence on users. We also found some unique benefits of each mechanism (e.g., Ambient Light could provide unobtrusive privacy awareness). We summarized four key design dimensions for future privacy awareness mechanisms design.

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. In this article, we first investigate “risky” child-directed voice apps that contain inappropriate content or ask for personal information through voice interactions. We build SkillBot—a natural language processing-based system to automatically interact with VPA apps and analyze the resulting conversations. We find 28 risky child-directed apps and maintain a growing dataset of 31,966 non-overlapping app behaviors collected from 3,434 Alexa apps. Our findings suggest that although child-directed VPA apps are subject to stricter policy requirements and more intensive vetting, children remain vulnerable to inappropriate content and privacy violations. We then conduct a user study showing that parents are concerned about the identified risky apps. Many parents do not believe that these apps are available and designed for families/kids, although these apps are actually published in Amazon’s “Kids” product category. We also find that parents often neglect basic precautions, such as enabling parental controls on Alexa devices. Finally, we identify a novel risk in the VPA ecosystem: confounding utterances or voice commands shared by multiple apps that may cause a user to interact with a different app than intended. We identify 4,487 confounding utterances, including 581 shared by child-directed and non-child-directed apps. We find that 27% of these confounding utterances prioritize invoking a non-child-directed app over a child-directed app. This indicates that children are at real risk of accidentally invoking non-child-directed apps due to confounding utterances.

Many Internet of Things devices have voice user interfaces. One of the most popular voice user interfaces is Amazon’s Alexa, which supports more than 50,000 third-party applications (“skills”). We study how Alexa’s integration of these skills may confuse users. Our survey of 237 participants found that users do not understand that skills are often operated by third parties, that they often confuse third-party skills with native Alexa functions, and that they are unaware of the functions that the native Alexa system supports. Surprisingly, users who interact with Alexa more frequently are more likely to conclude that a third-party skill is a native Alexa function. The potential for misunderstanding creates new security and privacy risks: attackers can develop third-party skills that operate without users’ knowledge or masquerade as native Alexa functions. To mitigate this threat, we make design recommendations to help users better distinguish native functionality and third-party skills, including audio and visual indicators of native and third-party contexts, as well as a consistent design standard to help users learn what functions are and are not possible on Alexa.

The number of Internet-connected TV devices has grown significantly in recent years, especially Over-the-Top ("OTT") streaming devices, such as Roku TV and Amazon Fire TV. OTT devices offer an alternative to multi-channel television subscription services, and are often monetized through behavioral advertising. To shed light on the privacy practices of such platforms, we developed a system that can automatically download OTT apps (also known as channels), and interact with them while intercepting the network traffic and performing best-effort TLS interception. We used this smart crawler to visit more than 2,000 channels on two popular OTT platforms, namely Roku and Amazon Fire TV. Our results show that tracking is pervasive on both OTT platforms, with traffic to known trackers present on 69% of Roku channels and 89% of Amazon Fire TV channels. We also discover widespread practice of collecting and transmitting unique identifiers, such as device IDs, serial numbers, WiFi MAC addresses and SSIDs, at times over unencrypted connections. Finally, we show that the countermeasures available on these devices, such as limiting ad tracking options and adblocking, are practically ineffective. Based on our findings, we make recommendations for researchers, regulators, policy makers, and platform/app developers.

We consider the problem of regulating products with negative externalities to a third party that is neither the buyer nor the seller, but where both the buyer and seller can take steps to mitigate the externality. The motivating example to have in mind is the sale of Internet-of-Things (IoT) devices, many of which have historically been compromised for DDoS attacks that disrupted Internet-wide services such as Twitter [5, 26]. Neither the buyer (i.e., consumers) nor seller (i.e., IoT manufacturers) was known to suffer from the attack, but both have the power to expend effort to secure their devices. We consider a regulator who regulates payments (via fines if the device is compromised, or market prices directly), or the product directly via mandatory security requirements.

Abstract The proliferation of smart home Internet of things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider) can infer private in-home activities by analyzing Internet traffic from commercially available smart home devices even when the devices use end-to-end transport-layer encryption. We evaluate common approaches for defending against these types of traffic analysis attacks, including firewalls, virtual private networks, and independent link padding, and find that none sufficiently conceal user activities with reasonable data overhead. We develop a new defense, “stochastic traffic padding” (STP), that makes it difficult for a passive network adversary to reliably distinguish genuine user activities from generated traffic patterns designed to look like user interactions. Our analysis provides a theoretical bound on an adversary’s ability to accurately detect genuine user activities as a function of the amount of additional cover traffic generated by the defense technique.

Ransomware is a type of malware that encrypts the files of infected hosts and demands payment, often in a crypto-currency like Bitcoin. In this paper, we create a measurement framework that we use to perform a large-scale, two-year, end-to-end measurement of ransomware payments, victims, and operators. By combining an array of data sources, including ransomware binaries, seed ransom payments, victim telemetry from infections, and a large database of bitcoin addresses annotated with their owners, we sketch the outlines of this burgeoning ecosystem and associated third-party infrastructure. In particular, we are able to trace the financial transactions, from the acquisition of bitcoins by victims, through the payment of ransoms, to the cash out of bitcoins by the ransomware operators. We find that many ransomware operators cashed out using BTC-e, a now-defunct Bitcoin exchange. In total we are able to track over $16 million USD in likely ransom payments made by 19,750 potential victims during a two-year period. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal operations that have similarly adopted Bitcoin as their payment channel.

Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.

In this paper, we investigate a new form of blackhat search engine optimization that targets local listing services like Google Maps. Miscreants register abusive business listings in an attempt to siphon search traffic away from legitimate businesses and funnel it to deceptive service industries---such as unaccredited locksmiths---or to traffic-referral scams, often for the restaurant and hotel industry. In order to understand the prevalence and scope of this threat, we obtain access to over a hundred-thousand business listings on Google Maps that were suspended for abuse. We categorize the types of abuse affecting Google Maps; analyze how miscreants circumvented the protections against fraudulent business registration such as postcard mail verification; identify the volume of search queries affected; and ultimately explore how miscreants generated a profit from traffic that necessitates physical proximity to the victim. This physical requirement leads to unique abusive behaviors that are distinct from other online fraud such as pharmaceutical and luxury product scams.