Jiameng Shi (University of Georgia), Wenqiang Li (Independent Researcher), Wenwen Wang (University of Georgia), Le Guan (University of Georgia)

Although numerous dynamic testing techniques have been developed, they can hardly be directly applied to firmware of deeply embedded (e.g., microcontroller-based) devices due to the tremendously different runtime environment and restricted resources on these devices. This work tackles these challenges by leveraging the unique position of microcontroller devices during firmware development. That is, firmware developers have to rely on a powerful engineering workstation that connects to the target device to program and debug code. Therefore, we develop a decoupled firmware testing framework named IPEA, which shifts the overhead of resource-intensive analysis tasks from the microcontroller to the workstation. Only lightweight “needle probes” are left in the firmware to collect internal execution information without processing it. We also instantiated this framework with a sanitizer based on pointer capability (IPEA-San) and a greybox fuzzer (IPEA-Fuzz). By comparing IPEA-San with a port of AddressSanitizer for microcontrollers, we show that IPEA-San reduces memory overhead by 62.75% in real-world firmware with better detection accuracy. Combining IPEA-Fuzz with IPEA-San, we found 7 zero-day bugs in popular IoT libraries (3) and peripheral driver code (4).

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MirageFlow: A New Bandwidth Inflation Attack on Tor

Christoph Sendner (University of Würzburg), Jasper Stang (University of Würzburg), Alexandra Dmitrienko (University of Würzburg), Raveen Wijewickrama (University of Texas at San Antonio), Murtuza Jadliwala (University of Texas at San Antonio)

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DeepGo: Predictive Directed Greybox Fuzzing

Peihong Lin (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Wei Xie (National University of Defense Technology), Gen Zhang (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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Modeling and Detecting Internet Censorship Events

Elisa Tsai (University of Michigan), Ram Sundara Raman (University of Michigan), Atul Prakash (University of Michigan), Roya Ensafi (University of Michigan)

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