USENIX Security '19 Technical Sessions

Bluetooth Low Energy (BLE) is a fast-growing wireless technology with a large number of potential use cases, particularly in the IoT domain. Increasingly, these use cases require the storage of sensitive user data or critical device controls on the BLE device, as well as the access of this data by an augmentative mobile application. Uncontrolled access to such data could violate user privacy, cause a device to malfunction, or even endanger lives. The BLE standard provides security mechanisms such as pairing and bonding to protect sensitive data such that only authenticated devices can access it. In this paper we show how unauthorized co-located Android applications can access pairing-protected BLE data, without the user's knowledge. We discuss mitigation strategies in terms of the various stakeholders involved in this ecosystem, and argue that at present, the only possible option for securing BLE data is for BLE developers to implement remedial measures in the form of application-layer security between the BLE device and the Android application. We introduce BLECryptracer, a tool for identifying the presence of such application-layer security, and present the results of a large-scale static analysis over 18,900+ BLE-enabled Android applications. Our findings indicate that over 45% of these applications do not implement measures to protect BLE data, and that cryptography is sometimes applied incorrectly in those that do. This implies that a potentially large number of corresponding BLE peripheral devices are vulnerable to unauthorized data access.

Apple Wireless Direct Link (AWDL) is a key protocol in Apple's ecosystem used by over one billion iOS and macOS devices for device-to-device communications. AWDL is a proprietary extension of the IEEE 802.11 (Wi-Fi) standard and integrates with Bluetooth Low Energy (BLE) for providing services such as Apple AirDrop. We conduct the first security and privacy analysis of AWDL and its integration with BLE. We uncover several security and privacy vulnerabilities ranging from design flaws to implementation bugs leading to a man-in-the-middle (MitM) attack enabling stealthy modification of files transmitted via AirDrop, denial-of-service (DoS) attacks preventing communication, privacy leaks that enable user identification and long-term tracking undermining MAC address randomization, and DoS attacks enabling targeted or simultaneous crashing of all neighboring devices. The flaws span across AirDrop's BLE discovery mechanism, AWDL synchronization, UI design, and Wi-Fi driver implementation. Our analysis is based on a combination of reverse engineering of protocols and code supported by analyzing patents. We provide proof-of-concept implementations and demonstrate that the attacks can be mounted using a low-cost ($20) micro:bit device and an off-the-shelf Wi-Fi card. We propose practical and effective countermeasures. While Apple was able to issue a fix for a DoS attack vulnerability after our responsible disclosure, the other security and privacy vulnerabilities require the redesign of some of their services.

Mobile autonomous systems, robots, and cyber-physical systems rely on accurate positioning information. To conduct distance-measurement, two devices exchange signals and, knowing these signals propagate at the speed of light, the time of arrival is used for distance estimations. Existing distance-measurement techniques are incapable of protecting against adversarial distance enlargement---a highly devastating tactic in which the adversary reissues a delayed version of the signals transmitted between devices, after distorting the authentic signal to prevent the receiver from identifying it. The adversary need not break crypto, nor compromise any upper-layer security protocols for mounting this attack. No known solution currently exists to protect against distance enlargement. We present \textit{Ultra-Wideband Enlargement Detection} (UWB-ED), a new modulation technique to detect distance enlargement attacks, and securely verify distances between two mutually trusted devices. We analyze UWB-ED under an adversary that injects signals to block/modify authentic signals. We show how UWB-ED is a good candidate for 802.15.4z Low Rate Pulse and the 5G standard.

Run-time attacks against programs written in memory-unsafe programming languages (e.g., C and C++) remain a prominent threat against computer systems. The prevalence of techniques like return-oriented programming (ROP) in attacking real-world systems has prompted major processor manufacturers to design hardware-based countermeasures against specific classes of run-time attacks. An example is the recently added support for pointer authentication (PA) in the ARMv8-A processor architecture, commonly used in devices like smartphones. PA is a low-cost technique to authenticate pointers so as to resist memory vulnerabilities. It has been shown to enable practical protection against memory vulnerabilities that corrupt return addresses or function pointers. However, so far, PA has received very little attention as a general purpose protection mechanism to harden software against various classes of memory attacks. In this paper, we use PA to build novel defenses against various classes of run-time attacks, including the first PA-based mechanism for data pointer integrity. We present PARTS, an instrumentation framework that integrates our PA-based defenses into the LLVM compiler and the GNU/Linux operating system and show, via systematic evaluation, that PARTS provides better protection than current solutions at a reasonable performance overhead.

Modern computer systems are becoming faster, more efficient, and increasingly interconnected with each generation. Thus, these platforms grow more complex, with new features continually introducing the possibility of new bugs. Although the semiconductor industry employs a combination of different verification techniques to ensure the security of System-on-Chip (SoC) designs, a growing number of increasingly sophisticated attacks are starting to leverage cross-layer bugs. These attacks leverage subtle interactions between hardware and software, as recently demonstrated through a series of real-world exploits that affected all major hardware vendors.

In this paper, we take a deep dive into microarchitectural security from a hardware designer’s perspective by reviewing state-of-the-art approaches used to detect hardware vulnerabilities at design time. We show that a protection gap currently exists, leaving chip designs vulnerable to software-based attacks that can exploit these hardware vulnerabilities. Inspired by real-world vulnerabilities and insights from our industry collaborator (a leading chip manufacturer), we construct the first representative testbed of real-world software-exploitable RTL bugs based on RISC-V SoCs. Patching these bugs may not always be possible and can potentially result in a product recall. Based on our testbed, we conduct two extensive case studies to analyze the effectiveness of state-of-the-art security verification approaches and identify specific classes of vulnerabilities, which we call HardFails, which these approaches fail to detect. Through our work, we focus the spotlight on specific limitations of these approaches to propel future research in these directions. We envision our RISC-V testbed of RTL bugs providing a rich exploratory ground for future research in hardware security verification and contributing to the open-source hardware landscape.

Research on transient execution attacks including Spectre and Meltdown showed that exception or branch misprediction events might leave secret-dependent traces in the CPU’s microarchitectural state. This observation led to a proliferation of new Spectre and Meltdown attack variants and even more ad-hoc defenses (e.g., microcode and software patches). Both the industry and academia are now focusing on finding effective defenses for known issues. However, we only have limited insight on residual attack surface and the completeness of the proposed defenses.

In this paper, we present a systematization of transient execution attacks. Our systematization uncovers 6 (new) transient execution attacks that have been overlooked and not been investigated so far: 2 new exploitable Meltdown effects: Meltdown-PK (Protection Key Bypass) on Intel, and Meltdown-BND (Bounds Check Bypass) on Intel and AMD; and 4 new Spectre mistraining strategies. We evaluate the attacks in our classification tree through proof-of-concept implementations on 3 major CPU vendors (Intel, AMD, ARM). Our systematization yields a more complete picture of the attack surface and allows for a more systematic evaluation of defenses. Through this systematic evaluation, we discover that most defenses, including deployed ones, cannot fully mitigate all attack variants.

Modern aircraft heavily rely on several wireless technologies for communications, control, and navigation. Researchers demonstrated vulnerabilities in many aviation systems. However, the resilience of the aircraft landing systems to adversarial wireless attacks have not yet been studied in the open literature, despite their criticality and the increasing availability of low-cost software-defined radio (SDR) platforms. In this paper, we investigate the vulnerability of aircraft instrument landing systems (ILS) to wireless attacks. We show the feasibility of spoofing ILS radio signals using commercially-available SDR, causing last-minute go around decisions, and even missing the landing zone in low-visibility scenarios. We demonstrate on aviation-grade ILS receivers that it is possible to fully and in fine-grain control the course deviation indicator as displayed by the ILS receiver, in real-time. We analyze the potential of both an overshadowing attack and a lower-power single-tone attack. In order to evaluate the complete attack, we develop a tightly-controlled closed-loop ILS spoofer that adjusts the adversary's transmitted signals as a function of the aircraft GPS location, maintaining power and deviation consistent with the adversary's target position, causing an undetected off-runway landing. We systematically evaluate the performance of the attack against an FAA certified flight-simulator (X-Plane)'s AI-based autoland feature and demonstrate systematic success rate with offset touchdowns of 18 meters to over 50 meters.

Modern vehicles contain tens of Electronic Control Units (ECUs), several of which communicate over the Controller Area Network (CAN) protocol. As such, in-vehicle networks have become a prime target for automotive network attacks. To understand the security of these networks, we argue that we need tools analogous to network mappers for traditional networks that provide an in-depth understanding of a network's structure. To this end, our goal is to develop an automotive network mapping tool that assists in identifying a vehicle's ECUs and their communication with each other. A significant challenge in designing this tool is the broadcast nature of the CAN protocol, as network messages contain no information about their sender or recipients. To address this challenge, we design and implement CANvas, an automotive network mapper that identifies transmitting ECUs with a pairwise clock offset tracking algorithm and identifies receiving ECUs with a forced ECU isolation technique. CANvas generates network maps in under an hour that identify a previously unknown ECU in a 2009 Toyota Prius and identify lenient message filters in a 2017 Ford Focus.

Robotic vehicles (RVs) are being adopted in a variety of application domains. Despite their increasing deployment, many security issues with RVs have emerged, limiting their wider deployment. In this paper, we address a new type of vulnerability in RV control programs, called input validation bugs, which involve missing or incorrect validation checks on control parameter inputs. Such bugs can be exploited to cause physical disruptions to RVs which may result in mission failures and vehicle damages or crashes. Furthermore, attacks exploiting such bugs have a very small footprint: just one innocent-looking ground control command, requiring no code injection, control flow hijacking or sensor spoofing. To prevent such attacks, we propose RVFuzzer, a vetting system for finding input validation bugs in RV control programs through control-guided input mutation. The key insight behind RVFuzzer is that the RV control model, which is the generic theoretical model for a broad range of RVs, provides helpful semantic guidance to improve bug-discovery accuracy and efficiency. Specifically, RVFuzzer involves a control instability detector that detects control program misbehavior, by observing (simulated) physical operations of the RV based on the control model. In addition, RVFuzzer steers the input generation for finding input validation bugs more efficiently, by leveraging results from the control instability detector as feedback. In our evaluation of RVFuzzer on two popular RV control programs, a total of 89 input validation bugs are found, with 87 of them being zero-day bugs.

Trusted Platform Modules are valuable building blocks for security solutions and have also been recognized as beneficial for security on mobile platforms, like smartphones and tablets. However, strict space, cost, and power constraints of mobile devices prohibit an implementation as dedicated on-board chip and the incumbent implementations are software TPMs protected by Trusted Execution Environments. In this paper, we present simTPM, an alternative implementation of a mobile TPM based on the SIM card available in mobile platforms. We solve the technical challenge of implementing a TPM2.0 in the resource-constrained SIM card environment and integrate our simTPM into the secure boot chain of the ARM Trusted Firmware on a HiKey960 reference board. Most notably, we address the challenge of how a removable TPM can be bound to the host device’s root of trust for measurement. As such, our solution not only provides a mobile TPM that avoids additional hardware while using a dedicated, strongly protected environment, but also offers promising synergies with co-existing TEE-based TPMs. In particular, simTPM offers a user-centric trusted module. Using performance benchmarks, we show that our simTPM has competitive speed with a reported TEE-based TPM and a hardware-based TPM.

Modern operating systems support a cooperating program abstraction that, instead of placing all functionality into a single program, allows diverse programs to cooperate to complete tasks requested by users. However, untrusted programs may exploit such interactions to spy on users through device sensors by causing privileged system services to misuse their permissions, or to forward user requests to malicious programs inadvertently. Researchers have previously explored methods to restrict access to device sensors based on the state of the user interface that elicited the user input or based on the set of cooperating programs, but the former approach does not consider cooperating programs and the latter approach has been found to be too restrictive for many cases. In this paper, we propose EnTrust, an authorization system that tracks the processing of input events across programs for eliciting approvals from users for sensor operations. EnTrust constructs delegation graphs by linking input events to cooperation events among programs that lead to sensor operation requests, then uses such delegation graphs for eliciting authorization decisions from users. To demonstrate this approach, we implement the EnTrust authorization system for Android OS. In a laboratory study, we show that attacks can be prevented at a much higher rate (47-67% improvement) compared to the first-use approach. Our field study reveals that EnTrust only requires a user e ort comparable to the first-use approach while incurring negligible performance (<1% slowdown) and memory overheads (5.5 KB per program).

Modern smartphone platforms implement permission-based models to protect access to sensitive data and system resources. However, apps can circumvent the permission model and gain access to protected data without user consent by using both covert and side channels. Side channels present in the implementation of the permission system allow apps to access protected data and system resources without permission; whereas covert channels enable communication between two colluding apps so that one app can share its permission-protected data with another app lacking those permissions. Both pose threats to user privacy.

In this work, we make use of our infrastructure that runs hundreds of thousands of apps in an instrumented environment. This testing environment includes mechanisms to monitor apps' runtime behaviour and network traffic. We look for evidence of side and covert channels being used in practice by searching for sensitive data being sent over the network for which the sending app did not have permissions to access it. We then reverse engineer the apps and third-party libraries responsible for this behaviour to determine how the unauthorized access occurred. We also use software fingerprinting methods to measure the static prevalence of the technique that we discover among other apps in our corpus.

Using this testing environment and method, we uncovered a number of side and covert channels in active use by hundreds of popular apps and third-party SDKs to obtain unauthorized access to both unique identifiers as well as geolocation data. We have responsibly disclosed our findings to Google and have received a bug bounty for our work.

Millions of users rely on mobile health (mHealth) apps to manage their wellness and medical conditions. Although the popularity of such apps continues to grow, several privacy and security challenges can hinder their potential. In particular, the simple fact that an mHealth app is installed on a user’s phone can reveal sensitive information about the user’s health. Due to Android’s open design, any app, even without permissions, can easily check for the presence of a specific app or collect the entire list of installed apps on the phone. Our analysis shows that Android apps expose a significant amount of metadata, which facilitates fingerprinting them. Many third parties are interested in such information: Our survey of 2917 popular apps in the Google Play Store shows that around 57% of these apps explicitly query for the list of installed apps. Therefore, we designed and implemented HideMyApp (HMA), an effective and practical solution for hiding the presence of sensitive apps from other apps. HMA does not require any changes to the Android operating system or to apps yet still supports their key functionalities. By using a diverse dataset of both free and paid mHealth apps, our experimental evaluation shows that HMA supports the main functionalities in most apps and introduces acceptable overheads at runtime (i.e., several milliseconds); these findings were validated by our user-study (N = 30). In short, we show that the practice of collecting information about installed apps is widespread and that our solution, HMA, provides a robust protection against such a threat.

Finding logic flaws today relies on the program analysis that leverages the functionality information reported in the program's documentation. Our research, however, shows that the documentation alone may already contain information for predicting the presence of some logic flaws, even before the code is analyzed. Our first step on this direction focuses on emerging syndication services that facilitate integration of multiple payment services (e.g., Alipay, Wechat Pay, PayPal, etc.) into merchant systems. We look at whether a syndication service will cause some security requirements (e.g., checking payment against price) to become unenforceable due to losing visibility of some key parameters (e.g., payment, price) to the parties involved in the syndication, or bring in implementation errors when required security checks fail to be communicated to the developer. For this purpose, we developed a suite of Natural Language Processing techniques that enables automatic inspection of the syndication developer’s guide, based upon the payment models and security requirements from the payment service. Our approach is found to be effective in identifying these potential problems from the guide, and leads to the discovery of 5 new security-critical flaws in popular Chinese merchant systems that can cause circumvention of payment once exploited.

Sharing files with specific users is a popular service provided by various widely used websites, e.g., Facebook, Twitter, Google, and Dropbox. A common way to ensure that a shared file can only be accessed by a specific user is to authenticate the user upon a request for the file. This paper shows a novel way of abusing shared image files for targeted privacy attacks. In our attack, called leaky images, an image shared with a particular user reveals whether the user is visiting a specific website. The basic idea is simple yet effective: an attacker-controlled website requests a privately shared image, which will succeed only for the targeted user whose browser is logged into the website through which the image was shared. In addition to targeted privacy attacks aimed at single users, we discuss variants of the attack that allow an attacker to track a group of users and to link user identities across different sites. Leaky images require neither JavaScript nor CSS, exposing even privacy-aware users, who disable scripts in their browser, to the leak. Studying the most popular websites shows that the privacy leak affects at least eight of the 30 most popular websites that allow sharing of images between users, including the three most popular of all sites. We disclosed the problem to the affected sites, and most of them have been fixing the privacy leak in reaction to our reports. In particular, the two most popular affected sites, Facebook and Twitter, have already fixed the leaky images problem. To avoid leaky images, we discuss potential mitigation techniques that address the problem at the level of the browser and of the image sharing website.

Many search engines have an autocomplete feature that presents a list of suggested queries to the user as they type. Autocomplete induces network traffic from the client upon changes to the query in a web page. We describe a remote keylogging attack on search engine autocomplete. The attack integrates information leaked by three independent sources: the timing of keystrokes manifested in packet inter-arrival times, percent-encoded Space characters in a URL, and the static Huffman code used in HTTP2 header compression. While each source is a relatively weak predictor in its own right, combined, and by leveraging the relatively low entropy of English language, up to 15% of search queries are identified among a list of 50 hypothesis queries generated from a dictionary with over 12k words. The attack succeeds despite network traffic being encrypted. We demonstrate the attack on two popular search engines and discuss some countermeasures to mitigate attack success.

The popularity of JavaScript has lead to a large ecosystem of third-party packages available via the npm software package registry. The open nature of npm has boosted its growth, providing over 800,000 free and reusable software packages. Unfortunately, this open nature also causes security risks, as evidenced by recent incidents of single packages that broke or attacked software running on millions of computers. This paper studies security risks for users of npm by systematically analyzing dependencies between packages, the maintainers responsible for these packages, and publicly reported security issues. Studying the potential for running vulnerable or malicious code due to third-party dependencies, we find that individual packages could impact large parts of the entire ecosystem. Moreover, a very small number of maintainer accounts could be used to inject malicious code into the majority of all packages, a problem that has been increasing over time. Studying the potential for accidentally using vulnerable code, we find that lack of maintenance causes many packages to depend on vulnerable code, even years after a vulnerability has become public. Our results provide evidence that npm suffers from single points of failure and that unmaintained packages threaten large code bases. We discuss several mitigation techniques, such as trusted maintainers and total first-party security, and analyze their potential effectiveness.

Automatic exploit generation is a challenging problem. A challenging part of the task is to connect an identified exploitable state (exploit primitive) to triggering execution of code-reuse (e.g., ROP) payload. A control-flow hijacking primitive is one of the most common capabilities for exploitation. However, due to the challenges of widely deployed exploit mitigations, pitfalls along an exploit path, and ill-suited primitives, it is difficult to even manually craft an exploit with a control-flow hijacking primitive for an off-the-shelf modern Linux kernel. We propose KEPLER to facilitate exploit generation by automatically generating a “single-shot” exploitation chain. KEPLER accepts as input a control-flow hijacking primitive and bootstraps any kernel ROP payload by symbolically stitching an exploitation chain taking advantage of prevalent kernel coding style and corresponding gadgets. Comparisons with previous automatic exploit generation techniques and previous kernel exploit techniques show KEPLER effectively facilitates evaluation of control-flow hijacking primitives in the Linux kernel.

Isolating sensitive state and data can increase the security and robustness of many applications. Examples include protecting cryptographic keys against exploits like OpenSSL's Heartbleed bug or protecting a language runtime from native libraries written in unsafe languages. When runtime references across isolation boundaries occur relatively infrequently, then conventional page-based hardware isolation can be used, because the cost of kernel- or hypervisor-mediated domain switching is tolerable. However, some applications, such as the isolation of cryptographic session keys in network-facing services, require very frequent domain switching. In such applications, the overhead of kernel- or hypervisor-mediated domain switching is prohibitive.

In this paper, we present ERIM, a novel technique that provides hardware-enforced isolation with low overhead on x86 CPUs, even at high switching rates (ERIM's measured overhead is less than 1% for 100,000 switches per second). The key idea is to combine protection keys (MPKs), a feature recently added to x86 that allows protection domain switches in userspace, with binary inspection to prevent circumvention. We show that ERIM can be applied with little effort to new and existing applications, doesn't require compiler changes, can run on a stock Linux kernel, and has low runtime overhead even at high domain switching rates.

AMD's Secure Encrypted Virtualization (SEV) is an emerging technology to secure virtual machines (VM) even in the presence of malicious hypervisors. However, the lack of trust in the privileged software also introduces an assortment of new attack vectors to SEV-enabled VMs that were mostly unexplored in the literature. This paper studies the insecurity of SEV from the perspective of the unprotected I/O operations in the SEV-enabled VMs. The results are alerting: not only have we discovered attacks that breach the confidentiality and integrity of these I/O operations---which we find very difficult to mitigate by existing approaches---but more significantly we demonstrate the construction of two attack primitives against SEV's memory encryption schemes, namely a memory decryption oracle and a memory encryption oracle, which enables an adversary to decrypt and encrypt arbitrary messages using the memory encryption keys of the VMs. We evaluate the proposed attacks and discuss potential solutions to the underlying problems.