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Trusted execution environments (TEEs) have been proposed to protect GPU computation for machine learning applications operating on sensitive data. However, existing GPU TEE solutions either require CPU and/or GPU hardware modification to realize TEEs for GPUs, which prevents current systems from adopting them, or rely on untrusted system software such as GPU device drivers. In this paper, we propose using CPU secure enclaves, e.g., Intel SGX, to build GPU TEEs without modifications to existing hardware. To tackle the fundamental limitations of these enclaves, such as no support for I/O operations, we design and develop GEVisor, a formally verified security reference monitor software to enable a trusted I/O path between enclaves and GPU without trusting the GPU device driver. GEVisor operates in the Virtual Machine Extension (VMX) root mode, monitors the host system software to prevent unauthorized access to the GPU code and data outside the enclave, and isolates the enclave GPU context from other contexts during GPU computation. We implement and evaluate GEVisor on a commodity machine with an Intel SGX CPU and an NVIDIA Pascal GPU. Our experimental results show that our approach maintains an average overhead of 13.1% for deep learning and 18% for GPU benchmarks compared to native GPU computation while providing GPU TEEs for existing CPU and GPU hardware. 

USB Power Delivery (USBPD) is a state-of-the-art charging protocol for advanced power supply. Thanks to its high volume of power supply, it has been widely adopted by consumer devices, such as smartphones and laptops, and has become the de facto USB charging standard in both EU and North America. Due to the low-level nature of charging and the complexity of the protocol, USBPD is often implemented as proprietary firmware running on a dedicated microcontroller unit (MCU) with a USBPD physical layer. Bugs within these implementations can not only lead to safety issues, e.g., over-charging, but also cause security issues, such as allowing attackers to reflash USBPD firmware. This paper proposes FUZZPD, the first black-box fuzzing technique with dual-role state guidance targeting off-the-shelf USBPD devices with closed-source USBPD firmware. FUZZPD only requires a physical USB Type-C connection to operate in a plug-n-fuzz fashion. To facilitate the black-box fuzzing of USBPD firmware, FUZZPD manually creates a dual-role state machine from the USBPD specification, which enables both state coverage and transitions from fuzzing inputs. FUZZPD further provides a multi-level mutation strategy, allowing for fine-grained state-aware fuzzing with intra- and inter-state mutations. We implement FUZZPD using a Chromebook as the fuzzing host and evaluate it against 12 USBPD mobile devices from 7 different vendors, 7 USB hubs from 7 different vendors, and 5 chargers from 5 different vendors. FUZZPD has found 15 unique bugs, 9 of which have been confirmed by the corresponding vendors. We additionally conduct a comparison between FUZZPD and multiple state-of-the-art black-box fuzzing techniques, demonstrating that FUZZPD achieves code coverage that is 40% to 3x higher than other solutions. We then compare FUZZPD with the USBPD compliance test suite from USBIF and show that FUZZPD can find 7 more bugs with 2x higher code coverage. FUZZPD is the first step towards secure and trustworthy USB charging. 

WebGL is a set of standardized JavaScript APIs for GPU-accelerated graphics. Security of the WebGL interface is paramount because it exposes remote and unsandboxed access to the underlying graphics stack (including the native GL libraries and GPU drivers) in the host OS. Unfortunately, applying state-of-the-art fuzzing techniques to the WebGL interface for vulnerability discovery is challenging because of (1) its huge input state space, and (2) the infeasibility of collecting code coverage across concurrent processes, closed-source libraries, and device drivers in the kernel. Our fuzzing technique, GLeeFuzz, guides input mutation by error messages instead of code coverage. Our key observation is that browsers emit meaningful error messages to aid developers in debugging their WebGL programs. Error messages indicate which part of the input fails (e.g., incomplete arguments, invalid arguments, or unsatisfied dependencies between API calls). Leveraging error messages as feedback, the fuzzer effectively expands coverage by focusing mutation on erroneous parts of the input. We analyze Chrome’s WebGL implementation to identify the dependencies between error-emitting statements and rejected parts of the input, and use this information to guide input mutation. We evaluate our GLeeFuzz prototype on Chrome, Firefox, and Safari on diverse desktop and mobile OSes. We discovered 7 vulnerabilities, 4 in Chrome, 2 in Safari, and 1 in Firefox. The Chrome vulnerabilities allow a remote attacker to freeze the GPU and possibly execute remote code at the browser privilege. 

iOS is one of the most valuable targets for security researchers. Unfortunately, studying the internals of this operating system is notoriously hard, due to the closed nature of the iOS ecosystem and the absence of easily-accessible analysis tools. To address this issue, we developed TruEMU, which we present in this talk. TruEMU is the first open-source, extensible, whole-system iOS emulator. Compared to the few available alternatives, TruEMU enables complete iOS kernel emulation, including emulation of the SecureROM and the USB kernel stack. More importantly, TruEMU is completely free and open-source, and it is based on the well-known and highly extensible emulator QEMU. This talk will start by presenting the challenges and the solutions we devised to reverse engineer current iOS boot code and kernel code, and explain how to provide adequate support in QEMU. Then, to showcase TruEMU's usefulness and capabilities, we will demonstrate how it can completely boot modern iOS images, including iOS 14 and the latest iOS 15, and how it can properly run different user-space components, such as launchd, restored, etc. Later, we will showcase two promising ways to use TruEMU as an iOS vulnerability research platform. Specifically, we will demonstrate how to use TruEMU to enable coverage-based fuzzing of the iOS kernel USB stack. Further, we will show how TruEMU provides a platform to implement coverage-based, syscall-level fuzzing. This platform enables security researchers to automatically explore multiple attack surfaces of iOS. In sum, building a complete emulator for iOS is a daunting task. Many features (i.e., many peripherals) still need to be implemented to allow a complete emulation of a modern iOS device. We hope this talk will also bootstrap a large community involvement in this project that will progressively shed more light on the obscure corners of iOS security. 

Controller Area Network (CAN) is the de-facto standard in-vehicle network system. Despite its wide adoption by automobile manufacturers, the lack of security design makes it vulnerable to attacks. For instance, broadcasting packets without authentication allows the impersonation of electronic control units (ECUs). Prior mitigations, such as message authentication or intrusion detection systems, fail to address the compatibility requirement with legacy ECUs, stealthy and sporadic malicious messaging, or guaranteed attack detection. We propose a novel authentication system called ShadowAuth that overcomes the aforementioned challenges by offering backward-compatible packet authentication to ECUs without requiring ECU firmware source code. Specifically, our authentication scheme provides transparent CAN packet authentication without modifying existing CAN packet definitions (e.g., J1939) via automatic ECU firmware instrumentation technique to locate CAN packet transmission code, and instrument authentication code based on the CAN packet behavioral transmission patterns. ShadowAuth enables vehicles to detect state-of-the-art CAN attacks, such as bus-off and packet injection, responsively within 60ms without false positives. ShadowAuth provides a sound and deployable solution for real-world ECUs.