The correct use of cryptography is central to ensuring data security in modern software systems. Hence, several academic and commercial static analysis tools have been developed for detecting and mitigating crypto-API misuse. While developers are optimistically adopting these crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a rigorous understanding of their effectiveness at finding crypto-API misuse in practice . This article describes the MASC framework, which enables a systematic and data-driven evaluation of crypto-detectors using mutation testing. We ground MASC in a comprehensive view of the problem space by developing a data-driven taxonomy of existing crypto-API misuse, containing 107 misuse cases organized among nine semantic clusters. We develop 19 generalizable usage-based mutation operators and three mutation scopes that can expressively instantiate thousands of compilable variants of the misuse cases for thoroughly evaluating crypto-detectors. Using MASC, in a previous study, we evaluated nine major crypto-detectors and discovered 19 unique, undocumented flaws that severely impact the ability of crypto-detectors to discover misuses in practice. This article substantially extends our MASC framework and offers updated evaluation of the crypto-detectors in our 2022 study, in addition to 5 more, major crypto-detectors . Through this work, we find 6 new, undocumented flaws, and demonstrate that these flaws affect the crypto-detectors regardless of their origin; open-source community, industry, and/or research. We conclude with a discussion on the diverse perspectives that influence the design of crypto-detectors and future directions towards building security-focused crypto-detectors by design.

As the popularity of Internet of Things (IoT) platforms grows, users gain unprecedented control over their homes, health monitoring, and daily task automation. However, the testing of software for these platforms poses significant challenges due to their diverse composition, e.g., common smart home platforms are often composed of varied types of devices that use a diverse array of communication protocols, connections to mobile apps, cloud services, as well as integration among various platforms. This paper is the first to uncover both the practices and perceptions behind testing in IoT platforms, particularly open-source smart home platforms. Our study is composed of two key components. First, we mine and empirically analyze the code and integrations of two highly popular and well maintained open-source IoT platforms, OpenHAB and HomeAssistant. Our analysis involves the identification of functional and related test methods based on the focal method approach. We find that OpenHAB has only 0.04 test ratio (~ 4K focal test methods from ~ 76K functional methods) in Java files, while HomeAssistant exhibits higher test ratio of 0.42, which reveals a significant dearth of testing. Second, to understand the developers' perspective on testing in IoT, and to explain our empirical observations, we survey 80 open-source developers actively engaged in IoT platform development. Our analysis of survey responses reveals a significant focus on automated (unit) testing, and a lack of manual testing, which supports our empirical observations, as well as testing challenges specific to IoT. Together, our empirical analysis and survey yield 10 key findings that uncover the current state of testing in IoT platforms, and reveal key perceptions and challenges. These findings provide valuable guidance to the research community in navigating the complexities of effectively testing IoT platforms.

As the regulatory landscape for the Internet of Things (IoT) evolves, vendors are moving towards certifying their products for security. Thus, we need to understand who is liable when certification failures result in harm, i.e., when certified products have vulnerabilities that are exploited to cause harm to users. This paper addresses the fundamental and timely question that has significant implications for vulnerability detection in certified products: who is liable for harm resulting from vulnerabilities in certified products, and who should be so liable? Through a qualitative analysis of contractual documents from 20 IoT vendors, this paper investigates how liability is currently defined in vendor-user contractual terms. This analysis then incorporates an expert survey of 18 legal professionals to examine their perspectives on liability within this context. Our analysis leads to 14 key findings (F1- F14) that show how vendors exclude liability to the maximum extent with (sometimes unlawful) exclusions, and how the perspectives of legal experts lie in stark contrast to what we observe in contracts (which are drafted by lawyers). We distill our findings into three key themes that call for a robust and clear liability framework, creating an incentive for IoT vendors to ensure that their IoT products meet proper security and privacy standards.

Regulators and standards bodies have recently proposed several security compliance initiatives for IoT products. These emerging standards and regulations seek to bring security assurance to IoT products by way of compliance certification. However, even certified IoT products exhibit common vulnerabilities, which suggests the presence of latent challenges in the certification ecosystem. This paper performs the first qualitative, interview-based study (n=17) with IoT practitioners to understand industry perspectives and experiences of IoT product security certification, in order to uncover the latent factors and challenges obstructing effective IoT product certification. Our reflexive thematic analysis of the interview transcripts leads to 16 key findings that uncover critical factors affecting compliance enforcement in practice. We distill these findings and our observations into 4 major themes which represent critical gaps that must be addressed for product certification to be viable for IoT.

City-wide free WiFi is one of the most common initiatives of smart city infrastructures. While city-wide free WiFi services are not subject to privacy-focused regulations and appeal to a broader demographic, how users perceive privacy in such services is unknown. This study explores perspectives of users in the United States regarding the privacy practices of such services as well as their expectations. We conducted surveys with 199 participants of US, consisting of those who had used such services (i.e., experienced users, n=99) and those who had not (i.e., potential users, n=100), assessing their satisfaction with the services, perceptions regarding data privacy practices of city-wide free WiFi services, and general expectations of privacy. We identify 14 key findings by analyzing the responses from participants. We found that participants are aware of the data collection and data sharing by the WiFi services and are uncomfortable with both but are still inclined to use the services as the need for WiFi outweighs privacy, as well as because of the significant trust they place in the services due to their non-profit and government-run nature. Our analysis provides actionable takeaways for researchers and practitioners, arguing for long-term privacy gains through a regulatory approach that treats city-wide WiFi as a utility, given the trust consumers place in it, and the overall tendency of consumers to trade-off privacy for WiFi access in this context.

The demand for automated security analysis techniques, such as static analysis based security testing (SAST) tools continues to increase. To develop SASTs that are effectively leveraged by developers for finding vulnerabilities, researchers and tool designers must understand how developers perceive, select, and use SASTs, what they expect from the tools, whether they know of the limitations of the tools, and how they address those limitations. This paper describes a qualitative study that explores the assumptions, expectations, beliefs, and challenges experienced by developers who use SASTs. We perform in-depth, semi-structured interviews with 20 practitioners who possess a diverse range of software development expertise, as well as a variety of unique security, product, and organizational backgrounds. We identify 17 key findings that shed light on developer perceptions and desires related to SASTs, and also expose gaps in the status quo – challenging long-held beliefs in SAST design priorities. Finally, we provide concrete future directions for researchers and practitioners rooted in an analysis of our findings.

Political campaigns are known to collect private user data, whether for building voter profiles, engaging with volunteers, or for soliciting donations. However, as such campaigns are classified as nonprofit in the United States (U.S.), their privacy practices have not received the same level of scrutiny as those of for-profit enterprises. This paper presents the Polityzer framework to evaluate the privacy posture of political campaign websites, and uses it to analyze 2060 campaign websites active during the U.S. election of November 2020. Our analysis leads to 20 key findings that demonstrate gaps in the privacy postures of political campaigns. For instance, we find that campaigns collect extensive private data they are not required to by the Federal Election Commission (FEC), and a vast majority do not provide any form of privacy disclosure. When disclosures are provided, they are often incomplete. We also found that campaigns may be inadvertently sharing data with other campaigns through common fundraising platforms, without disclosing such sharing. Reporting the lack of privacy disclosure to the respective campaigns yields further insights into the rationale behind their security posture. Finally, we discuss ways in which our results could enable future research, inform emerging privacy regulations, and transform user behavior regarding data privacy in this critical context.

Modern smart home platforms facilitate home automation using trigger-action routines. While providing flexibility, routines may also cause serious threats to system integrity: untrusted third-parties may use platform APIs to modify the abstract home objects (AHOs) that high-integrity devices (e.g., security camera) rely on (i.e., as triggers). As most AHO accesses are legitimate, applying naive information flow controls or removing permissions would not only fail to prevent these problems, but also break functionality. Therefore, this paper proposes the alternate approach of home abstraction endorsement, which endorses a proposed AHO change by correlating it with expected environmental changes. We present the HomeEndorser framework, which provides a policy model to express changes in device states as endorsement policy templates that are automatically instantiated in a given configuration (based on device availability/placement), and a platform-based reference monitor to mediate all API requests to change AHOs. We implement HomeEndorser as an enhancement to the HomeAssistant platform, and demonstrate less than 10% performance overhead and no false alarms under realistic usage, as well as derive policy templates for 6 key AHOs.

While software engineers are optimistically adopting crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a rigorous understanding of crypto-detectors’ effectiveness at finding crypto-API misuses in practice. This demo paper presents the technical details and usage scenarios of our tool, namely Mutation Analysis for evaluating Static Crypto-API misuse detectors (MASC). We developed 12 generalizable, usage based mutation operators and three mutation scopes, namely Main Scope, Similarity Scope, and Exhaustive Scope, which can be used to expressively instantiate compilable variants of the crypto-API misuse cases. Using MASC, we evaluated nine major crypto-detectors, and discovered 19 unique, undocumented flaws. We designed MASC to be configurable and user-friendly; a user can configure the parameters to change the nature of generated mutations. Furthermore, MASC comes with both Command Line Interface and Web-based front-end, making it practical for users of different levels of expertise.

Prior work has developed numerous systems that test the security and safety of smart homes. For these systems to be applicable in practice, it is necessary to test them with realistic scenarios that represent the use of the smart home, i.e., home automation, in the wild. This demo paper presents the technical details and usage of Helion, a system that uses n-gram language modeling to learn the regularities in user-driven programs, i.e., routines developed for the smart home, and predicts natural scenarios of home automation, i.e., event sequences that reflect realistic home automation usage. We demonstrate the HelionHA platform, developed by integrating Helion with the popular Home Assistant smart home platform. HelionHA allows an end-to-end exploration of Helion’s scenarios by executing them as test cases with real and virtual smart home devices.

Consumer IoT products and services are ubiquitous; yet, a proper characterization of consumer IoT security is infeasible without an understanding of what IoT products are on the market, i.e., without a market-scale perspective. This paper seeks to close this gap by developing the IoTSpotter framework, which automatically constructs a market-scale snapshot of mobile-IoT apps, i.e., mobile apps that are used as companions or automation providers to IoT devices. IoTSpotter also extracts artifacts that allow us to examine the security of this snapshot in the IoT context (e.g., devices supported by apps, IoT-specific libraries). Using IoTSpotter, we identify 37,783 mobile-IoT apps from Google Play, the largest set of mobile-IoT apps so far, and uncover 7 key results in the process (ℛ1-ℛ7). We leverage this dataset to perform three key security analyses that lead to 10 impactful security findings (F1-F10) that demonstrate the current state of mobile-IoT apps. Our analysis uncovers severe cryptographic violations in 94.11% (863/917) mobile-IoT apps with >1 million installs each, 65 vulnerable IoT-specific libraries affected by 79 unique CVEs, and used by 40 popular apps, and 7,887 apps that is affected by the Janus vulnerability. Finally, a case study with 18 popular mobile-IoT apps uncovers the critical impact of the vulnerabilities in them on important IoT artifacts and functions, motivating the development of mobile security analysis contextualized to IoT.

The correct use of cryptography is central to ensuring data security in modern software systems. Hence, several academic and commercial static analysis tools have been developed for detecting and mitigating crypto-API misuse. While developers are optimistically adopting these crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a rigorous understanding of their effectiveness at finding crypto-API misuse in practice. This paper presents the MASC framework, which enables a systematic and data-driven evaluation of crypto-detectors using mutation testing. We ground MASC in a comprehensive view of the problem space by developing a data-driven taxonomy of existing crypto-API misuse, containing 105 misuse cases organized among nine semantic clusters. We develop 12 generalizable usage-based mutation operators and three mutation scopes that can expressively instantiate thousands of compilable variants of the misuse cases for thoroughly evaluating crypto-detectors. Using MASC, we evaluate nine major crypto-detectors and discover 19 unique, undocumented flaws that severely impact the ability of crypto-detectors to discover misuses in practice. We conclude with a discussion on the diverse perspectives that influence the design of crypto-detectors and future directions towards building security-focused crypto-detectors by design.

Home automation platforms enable consumers to conveniently automate various physical aspects of their homes. However, the security flaws in the platforms or integrated third-party products can have serious security and safety implications for the user’s physical environment. This article describes our systematic security evaluation of two popular smart home platforms, Google’s Nest platform and Philips Hue, which implement home automation “routines” (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store . Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines, and it leads to 11 key findings with serious security implications. We combine several of the vulnerabilities we find to demonstrate the first end-to-end instance of lateral privilege escalation in the smart home, wherein we remotely disable the Nest Security Camera via a compromised light switch app. Finally, we discuss potential defenses, and the impact of the continuous evolution of smart home platforms on the practicality of security analysis. Our findings draw attention to the unique security challenges of smart home platforms and highlight the importance of enforcing security by design.

Mobile application security has been a major area of focus for security research over the course of the last decade. Numerous application analysis tools have been proposed in response to malicious, curious, or vulnerable apps. However, existing tools, and specifically, static analysis tools, trade soundness of the analysis for precision and performance and are hence sound y . Unfortunately, the specific unsound choices or flaws in the design of these tools is often not known or well documented, leading to misplaced confidence among researchers, developers, and users. This article describes the Mutation-Based Soundness Evaluation (μSE) framework, which systematically evaluates Android static analysis tools to discover, document, and fix flaws, by leveraging the well-founded practice of mutation analysis. We implemented μSE and applied it to a set of prominent Android static analysis tools that detect private data leaks in apps. In a study conducted previously, we used μSE to discover 13 previously undocumented flaws in FlowDroid, one of the most prominent data leak detectors for Android apps. Moreover, we discovered that flaws also propagated to other tools that build upon the design or implementation of FlowDroid or its components. This article substantially extends our μSE framework and offers a new in-depth analysis of two more major tools in our 2020 study; we find 12 new, undocumented flaws and demonstrate that all 25 flaws are found in more than one tool, regardless of any inheritance-relation among the tools. Our results motivate the need for systematic discovery and documentation of unsound choices in soundy tools and demonstrate the opportunities in leveraging mutation testing in achieving this goal.

Designing practical security systems for the smart home is challenging without the knowledge of realistic home usage. This paper describes the design and implementation of Hεlion, a framework that generates natural home automation scenarios by identifying the regularities in user-driven home automation sequences, which are in turn generated from routines created by end-users. Our key hypothesis is that smart home event sequences created by users exhibit inherent semantic patterns, or naturalness that can be modeled and used to generate valid and useful scenarios. To evaluate our approach, we first empirically demonstrate that this naturalness hypothesis holds, with a corpus of 30,518 home automation events, constructed from 273 routines collected from 40 users. We then demonstrate that the scenarios generated by Hεlion seem valid to end-users, through two studies with 16 external evaluators. We further demonstrate the usefulness of Hεlion's scenarios by addressing the challenge of policy specification, and using Hεlion to generate 17 security/safety policies with minimal effort. We distill 16 key findings from our results that demonstrate the strengths of our approach, surprising aspects of home automation, as well as challenges and opportunities in this rapidly growing domain.

Billions of users rely on the security of the Android platform to protect phones, tablets, and many different types of consumer electronics. While Android's permission model is well studied, the enforcement of the protection policy has received relatively little attention. Much of this enforcement is spread across system services, taking the form of hard-coded checks within their implementations. In this paper, we propose Authorization Check Miner (ACMiner), a framework for evaluating the correctness of Android's access control enforcement through consistency analysis of authorization checks. ACMiner combines program and text analysis techniques to generate a rich set of authorization checks, mines the corresponding protection policy for each service entry point, and uses association rule mining at a service granularity to identify inconsistencies that may correspond to vulnerabilities. We used ACMiner to study the AOSP version of Android 7.1.1 to identify 28 vulnerabilities relating to missing authorization checks. In doing so, we demonstrate ACMiner's ability to help domain experts process thousands of authorization checks scattered across millions of lines of code.

Home automation platforms provide a new level of convenience by enabling consumers to automate various aspects of physical objects in their homes. While the convenience is beneficial, security flaws in the platforms or integrated third-party products can have serious consequences for the integrity of a user's physical environment. In this paper we perform a systematic security evaluation of two popular smart home platforms, Google's Nest platform and Philips Hue, that implement home automation "routines" (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store. Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines. This analysis results in ten key findings with serious security implications. For instance, we demonstrate the potential for the misuse of smart home routines in the Nest platform to perform a lateral privilege escalation, illustrate how Nest's product review system is ineffective at preventing multiple stages of this attack that it examines, and demonstrate how emerging platforms may fail to provide even bare-minimum security by allowing apps to arbitrarily add/remove other apps from the user's smart home. Our findings draw attention to the unique security challenges of platforms that execute routines via centralized data stores, and highlight the importance of enforcing security by design in emerging home automation platforms.

Smartphones have become a primary form of computing. As a result, nearly every consumer, company, and organization provides an "app" for the popular smartphone platforms. Many of these apps are little more than a WebView widget that renders downloaded HTML and JavaScript content. In this paper, we argue that separating Web applications into separate OS principals has valuable security and privacy advantages. However, in the current smartphone application ecosystem, many such apps are fraught with privacy concerns. To this end, we propose NativeWrap as an alternative model for security and privacy conscious consumers to access Web content. NativeWrap "wraps" the domain for given URL into a native platform app, applying best practices for security configuration. We describe the design of a prototype of NativeWrap for the Android platform and test compatibility on the top 250 Alexa Websites. By using NativeWrap, third-party developers are removed from platform code, and users are placed in control of privacy sensitive operation.

Modern OSes such as Android, iOS, and Windows 8 have changed the way consumers interact with computing devices. Tasks are often completed by stringing together a collection of purpose-specific user applications (e.g., a barcode reader, a social networking app, a document viewer). As users direct this workflow between applications, it is difficult to predict the consequence of each step. Poor selection may result in accidental information disclosure when the target application unknowingly uses cloud services. This paper presents Aquifer as a policy framework and system for preventing accidental information disclosure in modern operating systems. In Aquifer, application developers define secrecy restrictions that protect the entire user interface workflow defining the user task. In doing so, Aquifer provides protection beyond simple permission checks and allows applications to retain control of data even after it is shared.