Zheyu Ma (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University; EPFL; JCSS, Tsinghua University (INSC) - Science City (Guangzhou) Digital Technology Group Co., Ltd.), Qiang Liu (EPFL), Zheming Li (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University; JCSS, Tsinghua University (INSC) - Science City (Guangzhou) Digital Technology Group Co., Ltd.), Tingting Yin (Zhongguancun Laboratory), Wende Tan (Department of Computer Science and Technology, Tsinghua University), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University; Zhongguancun Laboratory; JCSS, Tsinghua University (INSC) - Science City (Guangzhou) Digital Technology Group Co., Ltd.), Mathias Payer (EPFL)

Virtual devices are a large attack surface of hypervisors. Vulnerabilities in virtual devices may enable attackers to jailbreak hypervisors or even endanger co-located virtual machines. While fuzzing has discovered vulnerabilities in virtual devices across both open-source and closed-source hypervisors, the efficiency of these virtual device fuzzers remains limited because they are unaware of the complex behaviors of virtual devices in general. We present Truman, a novel universal fuzzing engine that automatically infers dependencies from open-source OS drivers to construct device behavior models (DBMs) for virtual device fuzzing, regardless of whether target virtual devices are open-source or binaries. The DBM includes inter- and intra-message dependencies and fine-grained state dependency of virtual device messages. Based on the DBM, Truman generates and mutates quality seeds that satisfy the dependencies encoded in the DBM. We evaluate the prototype of Truman on the latest version of hypervisors. In terms of coverage, Truman outperformed start-of-the-art fuzzers for 19/29 QEMU devices and obtained a relative coverage boost of 34% compared to Morphuzz for virtio devices. Additionally, Truman discovered 54 new bugs in QEMU, VirtualBox, VMware Workstation Pro, and Parallels, with 6 CVEs assigned.

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Pengzhi Xing (University of Electronic Science and Technology of China), Hongwei Li (University of Electronic Science and Technology of China), Meng Hao (Singapore Management University), Hanxiao Chen (University of Electronic Science and Technology of China), Jia Hu (University of Electronic Science and Technology of China), Dongxiao Liu (University of Electronic Science and Technology of China)

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DUMPLING: Fine-grained Differential JavaScript Engine Fuzzing

Liam Wachter (EPFL), Julian Gremminger (EPFL), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Mathias Payer (EPFL), Flavio Toffalini (EPFL)

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Spatial-Domain Wireless Jamming with Reconfigurable Intelligent Surfaces

Philipp Mackensen (Ruhr University Bochum), Paul Staat (Max Planck Institute for Security and Privacy), Stefan Roth (Ruhr University Bochum), Aydin Sezgin (Ruhr University Bochum), Christof Paar (Max Planck Institute for Security and Privacy), Veelasha Moonsamy (Ruhr University Bochum)

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Hitchhiking Vaccine: Enhancing Botnet Remediation With Remote Code Deployment...

Runze Zhang (Georgia Institute of Technology), Mingxuan Yao (Georgia Institute of Technology), Haichuan Xu (Georgia Institute of Technology), Omar Alrawi (Georgia Institute of Technology), Jeman Park (Kyung Hee University), Brendan Saltaformaggio (Georgia Institute of Technology)

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