Peripheral hardware in modern computers is typically assumed to be secure and not malicious, and device drivers are implemented in a way that trusts inputs from hardware. However, recent vulnerabilities such as Broadpwn have demonstrated that attackers can exploit hosts through vulnerable peripherals, highlighting the importance of securing the OS-peripheral boundary. In this paper, we propose a hardware-free concolic-augmented fuzzer targeting WiFi and Ethernet drivers, and a technique for generating high-quality initial seeds, which we call golden seeds, that allow fuzzing to bypass difficult code constructs during driver initialization. Compared to prior work using symbolic execution or greybox fuzzing, Drifuzz is more successful at automatically finding inputs that allow network interfaces to be fully initialized, and improves fuzzing coverage by 214% (3.1×) in WiFi drivers and 60% (1.6×) for Ethernet drivers. During our experiments with fourteen PCI and USB network drivers, we find twelve previously unknown bugs, two of which were assigned CVEs.
more »
« less
FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget Stacks
Universal Serial Bus (USB) is the de facto protocol supported by peripherals and mobile devices, such as USB thumb drives and smartphones. For many devices, USB Type-C ports are the primary interface for charging, file transfer, audio, video, etc. Accordingly, attackers have exploited different vulnerabilities within USB stacks, compromising host machines via BadUSB attacks or jailbreaking iPhones from USB connections. While there exist fuzzing frameworks dedicated to USB vulnerability discovery, all of them focus on USB host stacks and ignore USB gadget stacks, which enable all the features within modern peripherals and smart devices. In this paper, we propose FUZZUSB, the first fuzzing framework for the USB gadget stack within commodity OS kernels, leveraging static analysis, symbolic execution, and stateful fuzzing. FUZZUSB combines static analysis and symbolic execution to extract internal state machines from USB gadget drivers, and uses them to achieve state-guided fuzzing through multi-channel in- puts. We have implemented FUZZUSB upon the syzkaller kernel fuzzer and applied it to the most recent mainline Linux, Android, and FreeBSD kernels. As a result, we have found 34 previously unknown bugs within the Linux and Android kernels, and opened 7 CVEs. Furthermore, compared to the baseline, FUZZUSB has also demonstrated different improvements, including 3× higher code coverage, 50× improved bug-finding efficiency for Linux USB gadget stacks, 2× higher code coverage for FreeBSD USB gadget stacks, and reproducing known bugs that could not be detected by the baseline fuzzers. We believe FUZZUSB provides developers a powerful tool to thwart USB-related vulnerabilities within modern devices and complete the current USB fuzzing scope.
more »
« less
- Award ID(s):
- 1815883
- NSF-PAR ID:
- 10344259
- Date Published:
- Journal Name:
- 2022 IEEE Symposium on Security and Privacy (SP)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
As the number of Internet of Things (IoT) devices proliferate, an in-depth understanding of the IoT attack surface has become quintessential for dealing with the security and reliability risks. IoT devices and components execute implementations of various communication protocols. Vulnerabilities in the protocol stack implementations form an important part of the IoT attack surface. Therefore, finding memory errors in such implementations is essential for improving the IoT security and reliability. This paper presents a tool, SEESAW, that is built on top of a static analysis tool and a symbolic execution engine to achieve scalable analysis of protocol stack implementations. SEESAW leverages the API model of the analyzed code base to perform component-level analysis. SEESAW has been applied to the USB and Bluetooth modules within the Linux kernel. SEESAW can reproduce known memory vulnerabilities in a more scalable way compared to baseline symbolic execution.more » « less
-
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 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.more » « less
-
Fuzzing reliably and efficiently finds bugs in software, including operating system kernels. In general, higher code coverage leads to the discovery of more bugs. This is why most existing kernel fuzzers adopt strategies to generate a series of inputs that attempt to greedily maximize the amount of code that they exercise. However, simply executing code may not be sufficient to reveal bugs that require specific sequences of actions. Synthesizing inputs to trigger such bugs depends on two aspects: (i) the actions the executed code takes, and (ii) the order in which those actions are taken. An action is a high-level operation, such as a heap allocation, that is performed by the executed code and has a specific semantic meaning. ACTOR, our action-guided kernel fuzzing framework, deviates from traditional methods. Instead of focusing on code coverage optimization, our approach generates fuzzer programs (inputs) that leverage our understanding of triggered actions and their temporal relationships. Specifically, we first capture actions that potentially operate on shared data structures at different times. Then, we synthesize programs using those actions as building blocks, guided by bug templates expressed in our domain-specific language. We evaluated ACTOR on four different versions of the Linux kernel, including two well-tested and frequently updated long-term (5.4.206, 5.10.131) versions, a stable (5.19), and the latest (6.2-rc5) release. Our evaluation revealed a total of 41 previously unknown bugs, of which 9 have already been fixed. Interestingly, 15 (36.59%) of them were discovered in less than a day.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SP46214.2022.00037
