In today's era of the Internet of Things, we are surrounded by security- and safety-critical, network-connected devices. In parallel with the rise in attacks on such devices, we have also seen an increase in devices that are abandoned, reached the end of their support periods, or will not otherwise receive future security updates. While this issue exists for a wide array of devices, those that use monolithic firmware, where the code and data are opaquely intermixed, have traditionally been difficult to examine and protect. In this paper, we explore the challenges of retrofitting monolithic firmware images with new security measures. First, we outline the steps any analyst must take to retrofit firmware, and show that previous work is missing crucial aspects of the process, which are required for a practical solution. We then automate three of these aspects-locating attacker-controlled input, a safe retrofit injection location, and self-checks preventing modifications-through the use of novel automated program analysis techniques. We assemble these analyses into a system, Shimware, that can simplify and facilitate the process of creating a retrofitted firmware image, once the vulnerability is identified. To evaluate Shimware, we employ both a synthetic evaluation and actual retrofitting of three case study devices: a networked bench power supply, a Bluetooth-enabled cardiac implant monitor, and a high-end programmable logic controller (PLC). Not only could our system identify the correct sources of input, injection locations, and self-checks, but it injected payloads to correct serious safety and security-critical vulnerabilities in these devices.

As embedded devices are becoming more pervasive in our everyday lives, they turn into an attractive target for adver-saries. Despite their high value and large attack surface, ap-plying automated testing techniques such as fuzzing is not straightforward for such devices. As fuzz testing firmware on constrained embedded devices is inefficient, state-of-the-art approaches instead opt to run the firmware in an emulator (through a process called re-hosting ). However, existing approaches either use coarse-grained static models of hardware behavior or require manual effort to re-host the firmware. We propose a novel combination of lightweight program analysis, re-hosting, and fuzz testing to tackle these challenges. We present the design and implementation of F UZZWARE , a software-only system to fuzz test unmodified monolithic firmware in a scalable way. By determining how hardware-generated values are actually used by the firmware logic, F UZZWARE can automatically generate models that help fo-cusing the fuzzing process on mutating the inputs that matter, which drastically improves its effectiveness. We evaluate our approach on synthetic and real-world targets comprising a total of 19 hardware platforms and 77 firmware images. Compared to state-of-the-art work, F UZZ WARE achieves up to 3.25 times the code coverage and our modeling approach reduces the size of the input space by up to 95.5%. The synthetic samples contain 66 unit tests for various hardware interactions, and we find that our approach is the first generic re-hosting solution to automatically pass all of them. F UZZWARE discovered 15 completely new bugs including bugs in targets which were previously analyzed by other works;

Protecting the confidentiality of applications on commodity operating systems, both on desktop and mobile devices, is challenging: attackers have unrestricted control over an application's processes and thus direct access to any of the application's assets. However, the application's code itself can be of great commercial value, for example in the case of proprietary code or additional functionality obtained as downloadable content and via in-app purchases, which are widely used to monetize free applications through premium content. Developers still rely heavily on obfuscation to protect their own code from unauthorized tampering or copying, providing an obstacle for an attacker, but not preventing compromise. In this paper, we present Tarnhelm, an approach to offer a practical and transparent primitive to implement code confidentiality by extending ARM's TrustZone, a TEE that so far provides limited functionality to application developers. Tarnhelm allows developers to easily designate part of their code as confidential through source code annotations. At compile time, Tarnhelm automatically partitions the application into regular application code, executed in the "normal world," and the invisible code, transparently executed in the "secure world." Tarnhelm tightly couples and secures the execution in both worlds without exposing any additional attack surface by combining a number of different techniques, such as secure code loading, system call forwarding, transparent world switching, and the enforcement of inter-world control-flow integrity. We im- plemented a proof of concept of Tarnhelm and demonstrate its feasibility in a mobile computing setting.

Symbolic execution is a powerful technique for exploring programs and generating inputs that drive them into specific states. However, symbolic execution is also known to suffer from severe limitations, which prevent its application to real-world software. For example, symbolically executing programs requires modeling their interactions with the surrounding environment (e.g., libraries, operating systems). Unfortunately, models are usually created manually, introducing considerable approximations of the programs behaviors and significant imprecision in the analysis. In addition, as the complexity of the system under analysis grows, additional models are needed, making this process unsustainable. For these reasons, in this paper we propose a novel technique that allows interleaving symbolic execution with concrete execution, focusing the symbolic exploration only on interesting portions of code. We call this approach interleaved symbolic execution. The key idea of our approach is to re-use the concrete environment to run the input program, and then synchronize the results of the environment interactions with the symbolic execution engine. As a consequence, our approach does not make any assumption about such interactions, and it is agnostic with respect to the concrete environment. We implement a prototype for this technique, SYMBION, and we demonstrate its effectiveness by analyzing real-world malware, showing that it allows us to effectively skip complex portions of code that do not need to be analyzed symbolically.

Given the increasing ubiquity of online embedded devices, analyzing their firmware is important to security, privacy, and safety. The tight coupling between hardware and firmware and the diversity found in embedded systems makes it hard to perform dynamic analysis on firmware. However, firmware developers regularly develop code using abstractions, such as Hardware Abstraction Layers (HALs), to simplify their job. We leverage such abstractions as the basis for the re-hosting and analysis of firmware. By providing high-level replacements for HAL functions (a process termed High-Level Emulation - HLE), we decouple the hardware from the firmware. This approach works by first locating the library functions in a firmware sample, through binary analysis, and then providing generic implementations of these functions in a full-system emulator. We present these ideas in a prototype system, HALucinator, able to re-host firmware, and allow the virtual device to be used normally. First, we introduce extensions to existing library matching techniques that are needed to identify library functions in binary firmware, to reduce collisions, and for inferring additional function names. Next, we demonstrate the re-hosting process, through the use of simplified handlers and peripheral models, which make the process fast, flexible, and portable between firmware samples and chip vendors. Finally, we demonstrate the practicality of HLE for security analysis, by supplementing HALucinator with the American Fuzzy Lop fuzzer, to locate multiple previously-unknown vulnerabilities in firmware middleware libraries.

Binary static analysis has seen a recent surge in interest, due to a rise in analysis targets for which no other method is appropriate, such as, embedded firmware. This has led to the proposal of a number of binary static analysis tools and techniques, handling various kinds of programs, and answering different research questions. While static analyis tools that focus on binaries inherit the undecidability of static analysis, they bring with them other challenges, particularly in dealing with the aliasing of code and data pointers. These tools may tackle these challenges in different ways, but unfortunately, there is currently no concrete means of comparing their effectiveness at solving these central, problem-independent aspects of static analysis. In this paper, we propose a new method for creating a dataset of real-world programs, paired with the ground truth for static analysis. Our approach involves the injection of synthetic “facts” into a set of open-source programs, consisting of new variables and their possible values. The analyses’ goal is then to evaluate the possible values of these facts at certain program points. As the facts are injected randomly within an arbitrarily-large set of programs, the kinds of data flows that can be measured are widely-varied in size and complexity. We implemented this idea as a prototype system, AUTOFACTS, and used it to create a ground truth dataset of 29 programs, with various types and number of facts, resulting in a total of 2,088 binaries (with 72 versions for each program). To our knowledge, this is the first dataset aimed at the problem-independent evaluation of static analysis tools, and we contribute all code and the dataset itself to the community as open-source.

The recent paradigm shift introduced by the Internet of Things (IoT) has brought embedded systems into focus as a target for both security analysts and malicious adversaries. Typified by their lack of standardized hardware, diverse software, and opaque functionality, IoT devices present unique challenges to security analysts due to the tight coupling between their firmware and the hardware for which it was designed. In order to take advantage of modern program analysis techniques, such as fuzzing or symbolic execution, with any kind of scale or depth, analysts must have the ability to execute firmware code in emulated (or virtualized) environments. However, these emulation environments are rarely available and are cumbersome to create through manual reverse engineering, greatly limiting the analysis of binary firmware. In this work, we explore the problem of firmware re-hosting, the process by which firmware is migrated from its original hardware environment into a virtualized one. We show that an approach capable of creating virtual, interactive environments in an automated manner is a necessity to enable firmware analysis at scale. We present the first proof-of-concept system aiming to achieve this goal, called PRETENDER, which uses observations of the interactions between the original hardware and the firmware to automatically create models of peripherals, and allows for the execution of the firmware in a fully-emulated environment. Unlike previous approaches, these models are interactive, stateful, and transferable, meaning they are designed to allow the program to receive and process new input, a requirement of many analyses. We demonstrate our approach on multiple hardware platforms and firmware samples, and show that the models are flexible enough to allow for virtualized code execution, the exploration of new code paths, and the identification of security vulnerabilities.

While online review services provide a two-way conversation between brands and consumers, malicious actors, including misbehaving businesses, have an equal opportunity to distort the reviews for their own gains. We propose OneReview, a method for locating fraudulent reviews, correlating data from multiple crowd-sourced review sites. Our approach utilizes Change Point Analysis to locate points at which a business' reputation shifts. Inconsistent trends in reviews of the same businesses across multiple websites are used to identify suspicious reviews. We then extract an extensive set of textual and contextual features from these suspicious reviews and employ supervised machine learning to detect fraudulent reviews. We evaluated OneReview on about 805K and 462K reviews from Yelp and TripAdvisor, respectively to identify fraud on Yelp. Supervised machine learning yields excellent results, with 97% accuracy. We applied the created model on suspicious reviews and detected about 62K fraudulent reviews (about 8% of all the Yelp reviews). We further analyzed the detected fraudulent reviews and their authors, and located several spam campaigns in the wild, including campaigns against specific businesses, as well as campaigns consisting of several hundreds of socially-networked untrustworthy accounts.

In the past few years, both the industry and the academic communities have developed several approaches to detect malicious Android apps. State-of-the-art research approaches achieve very high accuracy when performing malware detection on existing datasets. These approaches perform their malware classification tasks in an "offline" scenario, where malware authors cannot learn from and adapt their malicious apps to these systems. In real-world deployments, however, adversaries get feedback about whether their app was detected, and can react accordingly by transforming their code until they are able to influence the classification. In this work, we propose a new approach for detecting Android malware that is designed to be resilient to feature-unaware perturbations without retraining. Our work builds on two key ideas. First, we consider only a subset of the codebase of a given app, both for precision and performance aspects. For this paper, our implementation focuses exclusively on the loops contained in a given app. We hypothesize, and empirically verify, that the code contained in apps' loops is enough to precisely detect malware. This provides the additional benefits of being less prone to noise and errors, and being more performant. The second idea is to build a feature space by extracting a set of labels for each loop, and by then considering each unique combination of these labels as a different feature: The combinatorial nature of this feature space makes it prohibitively difficult for an attacker to influence our feature vector and avoid detection, without access to the specific model used for classification. We assembled these techniques into a prototype, called LoopMC, which can locate loops in applications, extract features, and perform classification, without requiring source code. We used LoopMC to classify about 20,000 benign and malicious applications. While focusing on a smaller portion of the program may seem counterintuitive, the results of these experiments are surprising: our system achieves a classification accuracy of 99.3% and 99.1% for the Malware Genome Project and VirusShare datasets respectively, which outperforms previous approaches. We also evaluated LoopMC, along with the related work, in the context of various evasion techniques, and show that our system is more resilient to evasion.

Outlier detection has been shown to be a promising machine learning technique for a diverse array of felds and problem areas. However, traditional, supervised outlier detection is not well suited for problems such as network intrusion detection, where proper labelled data is scarce. This has created a focus on extending these approaches to be unsupervised, removing the need for explicit labels, but at a cost of poorer performance compared to their supervised counterparts. Recent work has explored ways of making up for this, such as creating ensembles of diverse models, or even diverse learning algorithms, to jointly classify data. While using unsupervised, heterogeneous ensembles of learning algorithms has been proposed as a viable next step for research, the implications of how these ensembles are built and used has not been explored.

Modern mobile bootloaders play an important role in both the function and the security of the device. They help ensure the Chain of Trust (CoT), where each stage of the boot process verifies the integrity and origin of the following stage before executing it. This process, in theory, should be immune even to attackers gaining full control over the operating system, and should prevent persistent compromise of a device's CoT. However, not only do these bootloaders necessarily need to take untrusted input from an attacker in control of the OS in the process of performing their function, but also many of their verification steps can be disabled ("unlocked") to allow for development and user customization. Applying traditional analyses on bootloaders is problematic, as hardware dependencies hinder dynamic analysis, and the size, complexity, and opacity of the code involved preclude the usage of many previous techniques. In this paper, we explore vulnerabilities in both the design and implementation of mobile bootloaders. We examine bootloaders from four popular manufacturers, and discuss the standards and design principles that they strive to achieve. We then propose BOOTSTOMP, a multi-tag taint analysis resulting from a novel combination of static analyses and dynamic symbolic execution, designed to locate problematic areas where input from an attacker in control of the OS can compromise the bootloader's execution, or its security features. Using our tool, we find six previously-unknown vulnerabilities (of which five have been confirmed by the respective vendors), as well as rediscover one that had been previously-reported. Some of these vulnerabilities would allow an attacker to execute arbitrary code as part of the bootloader (thus compromising the entire chain of trust), or to perform permanent denial-of-service attacks. Our tool also identified two bootloader vulnerabilities that can be leveraged by an attacker with root privileges on the OS to unlock the device and break the CoT. We conclude by proposing simple mitigation steps that can be implemented by manufacturers to safeguard the bootloader and OS from all of the discovered attacks, using already-deployed hardware features.

Today's mobile applications increasingly rely on communication with a remote backend service to perform many critical functions, including handling user-specific information. This implies that some form of authentication should be used to associate a user with their actions and data. Since schemes involving tedious account creation procedures can represent "friction" for users, many applications are moving toward alternative solutions, some of which, while increasing usability, sacrifice security. This paper focuses on a new trend of authentication schemes based on what we call "device-public" information, which consists of properties and data that any application running on a device can obtain. While these schemes are convenient to users, since they require little to no interaction, they are vulnerable by design, since all the needed information to authenticate a user is available to any app installed on the device. An attacker with a malicious app on a user's device could easily hijack the user's account, steal private information, send (and receive) messages on behalf of the user, or steal valuable virtual goods. To demonstrate how easily these vulnerabilities can be weaponized, we developed a generic exploitation technique that first mines all relevant data from a victim's phone, and then transfers and injects them into an attacker's phone to fool apps into granting access to the victim's account. Moreover, we developed a dynamic analysis detection system to automatically highlight problematic apps. Using our tool, we analyzed 1,000 popular applications and found that 41 of them, including the popular messaging apps WhatsApp and Viber, were vulnerable. Finally, our work proposes solutions to this issue, based on modifications to the Android API.

In the past decade, we have come to rely on computers for various safety and security-critical tasks, such as securing our homes, operating our vehicles, and controlling our finances. To facilitate these tasks, chip manufacturers have begun including trusted execution environments (TEEs) in their processors, which enable critical code (e.g., cryptographic functions) to run in an isolated hardware environment that is protected from the traditional operating system (OS) and its applications. While code in the untrusted environment (e.g., Android or Linux) is forbidden from accessing any memory or state within the TEE, the code running in the TEE, by design, has unrestricted access to the memory of the untrusted OS and its applications. However, due to the isolation between these two environments, the TEE has very limited visibility into the untrusted environment’s security mechanisms (e.g., kernel vs. application memory). In this paper, we introduce BOOMERANG, a class of vulnerabilities that arises due to this semantic separation between the TEE and the untrusted environment. These vulnerabilities permit untrusted user-level applications to read and write any memory location in the untrusted environment, including security-sensitive kernel memory, by leveraging the TEE’s privileged position to perform the operations on its behalf. BOOMERANG can be used to steal sensitive data from other applications, bypass security checks, or even gain full control of the untrusted OS. To quantify the extent of this vulnerability, we developed an automated framework for detecting BOOMERANG bugs within the TEEs of popular mobile phones. Using this framework, we were able to confirm the existence of BOOMERANG on four different TEE platforms, affecting hundreds of millions of devices on the market today. Moreover, we confirmed that, in at least two instances, BOOMERANG could be leveraged to completely compromise the untrusted OS (i.e., Android). While the implications of these vulnerabilities are severe, defenses can be quickly implemented by vendors, and we are currently in contact with the affected TEE vendors to deploy adequate fixes. To this end, we evaluated the two most promising defense proposals and their inherent trade-offs. This analysis led the proposal of a novel BOOMERANG defense, addressing the major shortcomings of the existing defenses with minimal performance overhead. Our findings have been reported to and verified by the corresponding vendors, who are currently in the process of creating security patches.

Analyzing Android applications for malicious behavior is an important area of research, and is made difficult, in part, by the increasingly large number of applications available for the platform. While techniques exist to perform static analysis on a large number of applications, dynamic analysis techniques are relatively limited in scale due to the computational resources required to emulate the full Android system to achieve accurate execution. We present Andlantis, a scalable dynamic analysis system capable of processing over 3000 Android applications per hour. During this processing, the system is able to collect valuable forensic data, which helps reverse-engineers and malware researchers identify and understand anomalous application behavior. We discuss the results of running 1261 malware samples through the system, and provide examples of malware analysis performed with the resulting data.

With the growing popularity of Android smart phones, it is increasingly important to ensure the security of sensitive user information. A recent study found that approximately 26% of Android applications in Google Play can access personal data, such as contacts and email, and 42 percent, GPS location data [6]. While Android is known for giving the user control, it falls short when it comes to enabling and disabling the permissions on applications. Currently, the user is given the option to either give the application every permission it desires or not install it. While researchers have proposed approaches for allowing users to modify the permissions granted to applications, it is unclear how removing permissions would affect the behavior of current applications. At present, developers expect all requested permissions to be granted. In this paper, we take the first step to quantify the impact of enabling users to statically remove permissions on Android applications post-installation. We developed Pyandrazzi, a system for evaluating the effect of removing individual permissions from applications. Using Pyandrazzi, we evaluated how removing seven common permissions affect a set of randomly selected applications that request them. We found that approximately 5.8% of the 700 applications we tested crash after a permission is removed and investigated how the lack of certain permissions are handled more gracefully than others. Our results will help users make more informed decisions when removing permissions and help developers make their applications more robust to permission revocation.