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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.
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FUME: Fuzzing Message Queuing Telemetry Transport Brokers
Message Queuing Telemetry Transport (MQTT) is a popular communication protocol used to interconnect devices with considerable network restraints, such as those found in Internet of Things (IoT). MQTT directly impacts a large number of devices, but the software security of its server ("broker") implementations is not well studied. In this paper, we design, implement, and evaluate a novel fuzz testing model for MQTT. The fuzzer combines aspects of mutation guided fuzzing and generation guided fuzzing to rigorously exhaust the MQTT protocol and identify vulnerabilities in servers. We introduce Markov chains for mutation guided fuzzing and generation guided fuzzing that model the fuzzing engine according to a finite Bernoulli process. We implement "response feedback", a novel technique which monitors network and console activity to learn which inputs trigger new responses from the broker. In total, we found 7 major vulnerabilities across 9 different MQTT implementations, including 6 zero-day vulnerabilities and 2 CVEs. We show that when fuzzing these popular MQTT targets, our fuzzer compares favorably with other state-of-the-art fuzzing frameworks, such as BooFuzz and AFLNet.
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- Award ID(s):
- 1915780
- PAR ID:
- 10356980
- Date Published:
- Journal Name:
- IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
- Page Range / eLocation ID:
- 1699 to 1708
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/INFOCOM48880.2022.9796755
