Security and privacy-sensitive smartphone applications use trusted execution environments (TEEs) to protect sensitive operations from malicious code. By design, TEEs have privileged access to the entire system but expose little to no insight into their inner workings. Moreover, real-world TEEs enforce strict format and protocol interactions when communicating with trusted applications (TAs), which prohibits effective automated testing. TEEzz is the first TEE-aware fuzzing framework capable of effectively fuzzing TAs in situ on production smartphones, i.e., the TA runs in the encrypted and protected TEE and the fuzzer may only observe interactions with the TA but has no control over the TA's code or data. Unlike traditional fuzzing techniques, which monitor the execution of a program being fuzzed and view its memory after a crash, TEEzz only requires a limited view of the target. TEEzz overcomes key limitations of TEE fuzzing (e.g., lack of visibility into the executed TAs, proprietary exchange formats, and value dependencies of interactions) by automatically attempting to infer the field types and message dependencies of the TA API through its interactions, designing state- and typeaware fuzzing mutators, and creating an in situ, on-device fuzzer. Due to the limited availability of systematic fuzzing research for TAs on commercial-off-the-shelf (COTS) Android devices, we extensively examine existing solutions, explore their limitations, and demonstrate how TEEzz improves the state-of-the-art. First, we show that general-purpose kernel driver fuzzers are ineffective for fuzzing TAs. Then, we establish a baseline for fuzzing TAs using a ground-truth experiment. We show that TEEzz outperforms other blackbox fuzzers, can improve greybox approaches (if TAs source code is available), and even outperforms greybox approaches for stateful targets. We found 13 previously unknown bugs in the latest versions of OPTEE TAs in total, out of which TEEzz is the only fuzzer to trigger three. We also ran TEEzz on popular phones and found 40 unique bugs for which one CVE was assigned so far.

Emulation is at the core of many security analyses. However, emulating embedded systems is still not possible in most cases. To facilitate this critical analysis, we present Conware, a hardware emulation framework that can automatically generate models for hardware peripherals, which alleviates one of the major challenges currently hindering embedded systems emulation. Conware enables individual peripherals to be modeled, exported, and combined with other peripherals in a pluggable fashion. Conware achieves this by first obtaining a recording of the low-level hardware interactions between the firmware and the peripheral, using either existing methods or our source-code instrumentation technique. These recordings are then used to create high-fidelity automata representations of the peripheral using novel automata-generation techniques. The various models can then be merged to facilitate full-system emulation of any embedded firmware that uses any of the modeled peripherals, even if that specific firmware or its target hardware was never directly instrumented. Indeed, we demonstrate that Conware is able to successfully emulate a peripheral-heavy firmware binary that was never instrumented, by merging the models of six unique peripherals that were trained on a development board using only the vendor-provided example code.

Hardware fault injection, or glitching, attacks can compromise the security of devices even when no software vulnerabilities exist. Attempts to analyze the hardware effects of glitching are subject to the Heisenberg effect and there is typically a disconnect between what people “think” is possible and what is actually possible with respect to these attacks. In this work, we attempt to provide some clarity to the impacts of attacks and defenses for control-flow modification through glitching. First, we introduce a glitching emulation framework, which provides a scalable playground to test the effects of bit flips on specific instruction set architectures (ISAs) (i.e., the fault tolerance of the instruction encoding). Next, we examine real glitching experiments using the ChipWhisperer, a popular microcontroller using open-source glitching hardware. These real-world experiments provide novel insights into how glitching attacks are realized and might be defended against in practice. Finally, we present GLITCHRESISTOR, an open-source, software-based glitching defense tool that can automatically insert glitching defenses into any existing source code, in an architecture-independent way. We evaluated GLITCHRESISTOR, which integrates numerous software-only defenses against powerful and real-world glitching attacks. Our findings indicate that software-only defenses can be implemented with acceptable run-time and size overheads, while completely mitigating some single-glitch attacks, minimizing the likelihood of a successful multi-glitch attack (i.e., a success rate of 0.000306%), and detecting failed glitching attempts at a high rate (between 79.2% and 100%).

Low-power, single-purpose embedded devices (e.g., routers and IoT devices) have become ubiquitous. While they automate and simplify many aspects of users' lives, recent large-scale attacks have shown that their sheer number poses a severe threat to the Internet infrastructure. Unfortunately, the software on these systems is hardware-dependent, and typically executes in unique, minimal environments with non-standard configurations, making security analysis particularly challenging. Many of the existing devices implement their functionality through the use of multiple binaries. This multi-binary service implementation renders current static and dynamic analysis techniques either ineffective or inefficient, as they are unable to identify and adequately model the communication between the various executables. In this paper, we present KARONTE, a static analysis approach capable of analyzing embedded-device firmware by modeling and tracking multi-binary interactions. Our approach propagates taint information between binaries to detect insecure interactions and identify vulnerabilities. We first evaluated KARONTE on 53 firmware samples from various vendors, showing that our prototype tool can successfully track and constrain multi-binary interactions. This led to the discovery of 46 zero-day bugs. Then, we performed a large-scale experiment on 899 different samples, showing that KARONTE scales well with firmware samples of different size and complexity.

Cyber-physical systems (CPSes) have been replacing their mechanical counterparts in many safety- and security-critical applications (e.g., door locks, automobiles, and critical infrastructure). However, this paradigm shift has introduced a new software-based attack vector into these historically isolated systems. Since many of these devices are networked, their physical interfaces are vulnerable to both remote and local attackers. In this work, we present TRUST.IO, a framework that automatically, and transparently, hardens these physical interfaces against all software-based exploits. More precisely, TRUST.IO ensures that the software on the device cannot access any protected general purpose input/output (GPIO) interfaces unless the command was initiated from a trusted external client (e.g., a key, phone, or centralized server). TRUST.IO exploits the fact that users rarely interact directly with these embedded devices. Instead, users interact with a remote system (e.g., a car key, smart hub, or control system) that ultimately issues commands to the single-purpose embedded device. Thus, TRUST.IO leverages modern embedded processor features to ensure that these critical physical interactions (e.g., actuating motors or reading sensors) will be performed if and only if the command was issued by an authorized external device that can satisfy a cryptographic challenge. We demonstrate that TRUST.IO can be easily applied to existing CPSes, both bare-metal and Linux-based, with minimal runtime overhead and minimal code modifications.

Many control-flow-hijacking attacks rely on information leakage to disclose the location of gadgets. To address this, several leakage-resilient defenses, have been proposed that fundamentally limit the power of information leakage. Examples of such defenses include address-space re-randomization, destructive code reads, and execute-only code memory. Underlying all of these defenses is some form of code randomization. In this paper, we illustrate that randomization at the granularity of a page or coarser is not secure, and can be exploited by generalizing the idea of partial pointer overwrites, which we call the Relative ROP (RelROP) attack. We then analyzed more that 1,300 common binaries and found that 94% of them contained sufficient gadgets for an attacker to spawn a shell. To demonstrate this concretely, we built a proof-of-concept exploit against PHP 7.0.0. Furthermore, randomization at a granularity finer than a memory page faces practicality challenges when applied to shared libraries. Our findings highlight the dilemma that faces randomization techniques: course-grained techniques are efficient but insecure and fine-grained techniques are secure but impractical.

Research in genetic improvement (GI) conventionally focuses on the improvement of software, including the automated repair of bugs and vulnerabilities as well as the refinement of software to increase performance. Eliminating or reducing vulnerabilities using GI has improved the security of benign software, but the growing volume and complexity of malicious software necessitates better analysis techniques that may benefit from a GI-based approach. Rather than focus on the use of GI to improve individual software artifacts, we believe GI can be applied to the tools used to analyze malicious code for its behavior. First, malware analysis is critical to understanding the damage caused by an attacker, which GI-based bug repair does not currently address. Second, modern malware samples leverage complex vectors for infection that cannot currently be addressed by GI. In this paper, we discuss an application of genetic improvement to the realm of automated malware analysis through the use of variable-strength covering arrays.

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.

The OS kernel is an attractive target for remote attackers.If compromised, the kernel gives adversaries full system access, including the ability to install rootkits, extract sensitive information, and perform other malicious actions, all while evading detection.Most of the kernel's attack surface is situated along the system call boundary.Ongoing kernel protection efforts have focused primarily on securing this boundary; several capable analysis and fuzzing frameworks have been developed for this purpose.However, there are additional paths to kernel compromise that do not involve system calls, as demonstrated by several recent exploits.For example, by compromising the firmware of a peripheral device such as a Wi-Fi chipset and subsequently sending malicious inputs from the Wi-Fi chipset to the Wi-Fi driver, adversaries have been able to gain control over the kernel without invoking a single system call.Unfortunately, there are currently no practical probing and fuzzing frameworks that can help developers find and fix such vulnerabilities occurring along the hardware-OS boundary.We present PERISCOPE, a Linux kernel based probing framework that enables fine-grained analysis of device-driver interactions.PERISCOPE hooks into the kernel's page fault handling mechanism to either passively monitor and log traffic between device drivers and their corresponding hardware, or mutate the data stream on-the-fly using a fuzzing component, PERIFUZZ, thus mimicking an active adversarial attack.PER-IFUZZ accurately models the capabilities of an attacker on peripheral devices, to expose different classes of bugs including, but not limited to, memory corruption bugs and double-fetch bugs.To demonstrate the risk that peripheral devices pose, as well as the value of our framework, we have evaluated PERIFUZZ on the Wi-Fi drivers of two popular chipset vendors, where we discovered 15 unique vulnerabilities, 9 of which were previously unknown.

While kernel drivers have long been know to poses huge security risks, due to their privileged access and lower code quality, bug-finding tools for drivers are still greatly lacking both in quantity and effectiveness. This is because the pointer-heavy code in these drivers present some of the hardest challenges to static analysis, and their tight coupling with the hardware make dynamic analysis infeasible in most cases. In this work, we present DR. CHECKER, a soundy (i.e., mostly sound) bug-finding tool for Linux kernel drivers that is based on well-known program analysis techniques. We are able to overcome many of the inherent limitations of static analysis by scoping our analysis to only the most bug-prone parts of the kernel (i.e., the drivers), and by only sacrificing soundness in very few cases to ensure that our technique is both scalable and precise. DR. CHECKER is a fully-automated static analysis tool capable of performing general bug finding using both pointer and taint analyses that are flow-sensitive, context-sensitive, and fieldsensitive on kernel drivers. To demonstrate the scalability and efficacy of DR. CHECKER, we analyzed the drivers of nine production Linux kernels (3.1 million LOC), where it correctly identified 158 critical zero-day bugs with an overall precision of 78%.

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.

There is a growing need for the dynamic analysis of sensitive systems thatdo not support traditional debugging or emulation environments. Analysiscan alter program behavior, necessitating transparency. For example, asthe cat and mouse game between malware authors and malware analystsprogresses, malicious software can increasingly detect and confounddebuggers. Analysts must understand variable values, stack traces, andfactors influencing dynamic behavior, but recent malware samples leverageany piece of information or artifact available that signals the presence ofa debugger or emulator. In this work, we advance the state-of-the-art for transparent programanalysis by introducing a low-artifact introspection technique. Ourapproach uses hardware-assisted live memory snapshots ofprocess execution on native targets (e.g., x86 processors), coupledwith static reasoning about programs. We produce high-fidelity data and control flow information with minimaldetectable artifacts that could influence benign subject behavior or beleveraged for anti-analysis. We evaluate our system using two hardwareimplementations (x86-supported System Management Mode and PCI-basedSlotScreamer devices) and two software configurations (benign and evasiveprograms). We also analyze the theoretical and practical limitations of our technique. We discuss an expert case study in which we apply our technique to amalware reverse engineering task. Finally, we present results of a human study in which 30 participantsperformed debugging tasks using information provided by our approach, ourtool was as useful as a gdb baseline, but applies transparently. Our dynamic analysis approach permitstransparent introspection to access previously-unavailable informationabout a process's internal state with minimal instrumentation artifacts.

Dynamic-analysis techniques have become the linchpins of modern malware analysis. However, software-based methods have been shown to expose numerous artifacts, which can either be detected and subverted, or potentially interfere with the analysis altogether, making their results untrustworthy. The need for less-intrusive methods of analysis has led many researchers to utilize introspection in place of instrumenting the software itself. While most current introspection technologies have focused on virtual-machine introspection, we present a novel system, LO-PHI, which is capable of physical-machine introspection of both non-volatile and volatile memory, i.e., hard disk and system memory. We demonstrate that we are able to provide analysis capabilities comparable to existing solutions, whilst exposing zero software-based artifacts and minimal hardware artifacts. To demonstrate the usefulness of our system, we have developed a framework for performing automated binary analysis. We employ this framework to analyze numerous potentially malicious binaries using both traditional virtual-machine introspection and our new hardware-based instrumentation. Our results show that not only is our analysis on-par with existing software-based counterparts, but that our physical instrumentation is capable of successfully analyzing far more binaries, as it is not foiled by popular anti-analysis techniques.

Modern mobile devices place a wide variety of sensors and services within the personal space of their users. As a result, these devices are capable of transparently monitoring many sensitive aspects of these users’ lives (e.g., location, health, or correspondences). Users typically trade access to this data for convenient applications and features, in many cases without a full appreciation of the nature and extent of the information that they are exposing to a variety of third parties. Nevertheless, studies show that users remain concerned about their privacy and vendors have similarly been increasing their utilization of privacy-preserving technologies in these devices. Still, despite significant efforts, these technologies continue to fail in fundamental ways, leaving users’ private data exposed. In this work, we survey the numerous components of mobile devices, giving particular attention to those that collect, process, or protect users’ private data. Whereas the individual components have been generally well studied and understood, examining the entire mobile device ecosystem provides significant insights into its overwhelming complexity. The numerous components of this complex ecosystem are frequently built and controlled by different parties with varying interests and incentives. Moreover, most of these parties are unknown to the typical user. The technologies that are employed to protect the users’ privacy typically only do so within a small slice of this ecosystem, abstracting away the greater complexity of the system. Our analysis suggests that this abstracted complexity is the major cause of many privacy-related vulnerabilities, and that a fundamentally new, holistic, approach to privacy is needed going forward. We thus highlight various existing technology gaps and propose several promising research directions for addressing and reducing this complexity.

Abstract : Google Android and Apple iOS ecosystems differ fundamentally, largely because of their parent companies 'different business models. However, the platforms' tools and protections appear to be converging: Android is adopting run-time permissions requests, which iOS has used for years; both are incorporating stronger encryption.

Modern mobile devices place a wide variety of sensors and services within the personal space of their users. As a result, these devices are capable of transparently monitoring many sensitive aspects of these users’ lives (e.g., location, health, or correspondences). Users typically trade access to this data for convenient applications and features, in many cases without a full appreciation of the nature and extent of the information that they are exposing to a variety of third parties. Nevertheless, studies show that users remain concerned about their privacy and vendors have similarly been increasing their utilization of privacy-preserving technologies in these devices. Still, despite significant efforts, these technologies continue to fail in fundamental ways, leaving users’ private data exposed. In this work, we survey the numerous components of mobile devices, giving particular attention to those that collect, process, or protect users’ private data. Whereas the individual components have been generally well studied and understood, examining the entire mobile device ecosystem provides significant insights into its overwhelming complexity. The numerous components of this complex ecosystem are frequently built and controlled by different parties with varying interests and incentives. Moreover, most of these parties are unknown to the typical user. The technologies that are employed to protect the users’ privacy typically only do so within a small slice of this ecosystem, abstracting away the greater complexity of the system. Our analysis suggests that this abstracted complexity is the major cause of many privacy-related vulnerabilities, and that a fundamentally new, holistic, approach to privacy is needed going forward. We thus highlight various existing technology gaps and propose several promising research directions for addressing and reducing this complexity.

During a disaster, conventional communications infrastructures are often compromised, which prevents local populations from contacting family, friends, and colleagues. The lack of communication also impedes responder efforts to gather, organize, and disseminate information. This problem is made worse by the unique cost and operational constraints typically associated with the humanitarian assistance and disaster relief (HADR) space. In response, we present a low-cost, scalable system that creates a wide-area, best-effort, ad-hoc wireless network for emergency information. The Communication Assistance Technology over Ad-Hoc Networks (CATAN) system embraces the maker and do it yourself (DIY) communities by leveraging open-source and hobbyist technologies to create cheap, lightweight, battery powered nodes that can be deployed quickly for a variety of operations. CATAN enables geographically separated users to share information on standard interfaces, i.e. web and SMS, over commonly-used communication interfaces, i.e. GSM and Wi-Fi. These interfaces enable CATAN to accommodate a variety of digital devices while leveraging the global ubiquity of cellular devices. By emphasizing simple, mature, technologies, CATAN avoids many problems that hinder many general purpose ad-hoc technologies. We have tested our infrastructure in a variety of environments and have open-sourced the entire project to encourage collaboration with the greater HADR community.

Accesses that are not permitted by implemented policy but that share similarities with accesses that have been allowed, may be indicative of access-control policy misconfigurations. Identifying such misconfigurations allows administrators to resolve them before they interfere with the use of the system. We improve upon prior work in identifying such misconfigurations in two main ways. First, we develop a new methodology for evaluating misconfiguration prediction algorithms and applying them to real systems. We show that previous evaluations can substantially overestimate the benefits of using such algorithms in practice, owing to their tendency to reward predictions that can be deduced to be redundant. We also show, however, that these and other deductions can be harnessed to substantially recover the benefits of prediction. Second, we propose an approach that significantly simplifies the use of misconfiguration prediction algorithms. We remove the need to hand-tune (and empirically determine the effects of) various parameters, and instead replace them with a single, intuitive tuning parameter. We show empirically that this approach is generally competitive in terms of benefit and accuracy with algorithms that require hand-tuned parameters.

Accesses that are not permitted by implemented policy but that share similarities with accesses that have been allowed, may be indicative of access-control policy misconfigurations. Identifying such misconfigurations allows administrators to resolve them before they interfere with the use of the system. We improve upon prior work in identifying such misconfigurations in two main ways. First, we develop a new methodology for evaluating misconfiguration prediction algorithms and applying them to real systems. We show that previous evaluations can substantially overestimate the benefits of using such algorithms in practice, owing to their tendency to reward predictions that can be deduced to be redundant. We also show, however, that these and other deductions can be harnessed to substantially recover the benefits of prediction. Second, we propose an approach that significantly simplifies the use of misconfiguration prediction algorithms. We remove the need to hand-tune (and empirically determine the effects of) various parameters, and instead replace them with a single, intuitive tuning parameter. We show empirically that this approach is generally competitive in terms of benefit and accuracy with algorithms that require hand-tuned parameters.