Dominik Maier, Lukas Seidel (TU Berlin)

Researchers spend hours, or even days, to understand a target well enough to harness it and get a feedback-guided fuzzer running. Once this is achieved, they rely on their fuzzer to find the right paths, maybe sampling the collected queue entries to see how well it performs. Their knowledge is of little help to the fuzzer, while the fuzzer’s behavior is largely a black box to the researcher. Enter JMPscare, providing deep insight into fuzzing queues. By highlighting unreached basic blocks across all queue items during fuzzing, JMPscare allows security researchers to understand the shortcomings of their fuzzer and helps to overcome them. JMPscare can analyze thousands of queue entries efficiently and highlight interesting roadblocks, socalled frontiers. This intel helps the human-in-the-loop to improve the fuzzer, mutator, and harness. Even complex bugs, hard to reach for a generalized fuzzer, hidden deep in the control flow of the target, can be covered in this way. Apart from a purely analytical view, its convenient built-in binary patching facilitates forced execution for subsequent fuzz runs. We demonstrate the benefit of JMPscare on the ARM-based MediaTek Baseband. With JMPscare we gain an in-depth understanding of larger parts of the firmware and find new targets in this RTOS. JMPscare simplifies further mutator, fuzzer, and instrumentation development.

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Daniel Reijsbergen (Singapore University of Technology and Design), Pawel Szalachowski (Singapore University of Technology and Design), Junming Ke (University of Tartu), Zengpeng Li (Singapore University of Technology and Design), Jianying Zhou (Singapore University of Technology and Design)

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Guoming Zhang (Zhejiang University), Xiaoyu Ji (Zhejiang University), Xinfeng Li (Zhejiang University), Gang Qu (University of Maryland), Wenyuan Xu (Zhejing University)

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Jeremy Daily, David Nnaji, and Ben Ettlinger (Colorado State University)

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Florian Hofhammer (EPFL), Marcel Busch (EPFL), Qinying Wang (EPFL and Zhejiang University), Manuel Egele (Boston University), Mathias Payer (EPFL)

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Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

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Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

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Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)