Romain Malmain (EURECOM), Andrea Fioraldi (EURECOM), Aurelien Francillon (EURECOM)

Despite QEMU’s popularity for binary-only fuzzing, the fuzzing community faces challenges like the proliferation of hard-to-maintain QEMU forks and the lack of an up-to-date, flexible framework well-integrated with advanced fuzzing engines. This leads to a gap in emulation-based fuzzing tools that are both maintainable and fuzzing-oriented.

To cope with that, we present LIBAFL QEMU, a library written in Rust that provides an interface for fuzzing-based emulation by wrapping around QEMU, in both system and user mode. We focus on addressing the limitations of existing QEMU forks used in fuzzing by offering a well-integrated, maintainable and up-to-date solution. In this paper, we detail the design, implementation, and practical challenges of LIBAFL QEMU, including its APIs and fuzzing capabilities and we showcase the library’s use in two case studies: fuzzing an Android library and a Windows kernel driver.

We compare the fuzzers written for these 2 targets with the state-of-the-art, AFL++ qemu mode for the Android library, and KAFL for the Windows driver. For the former, we show that LIBAFL QEMU outperforms AFL++ qemu mode both in terms of speed and coverage. For the latter, despite KAFL being built above hardware-based virtualization instead of emulation, we show we can run complex targets such as Windows and still reach comparable performance, with an overhead expected by a software emulator.

View More Papers

Work-in-Progress: Manifest V3 Unveiled: Navigating the New Era of...

Nikolaos Pantelaios and Alexandros Kapravelos (North Carolina State University)

Read More

SyzBridge: Bridging the Gap in Exploitability Assessment of Linux...

Xiaochen Zou (UC Riverside), Yu Hao (UC Riverside), Zheng Zhang (UC RIverside), Juefei Pu (UC RIverside), Weiteng Chen (Microsoft Research, Redmond), Zhiyun Qian (UC Riverside)

Read More

SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

Read More

AutoWatch: Learning Driver Behavior with Graphs for Auto Theft...

Paul Agbaje, Abraham Mookhoek, Afia Anjum, Arkajyoti Mitra (University of Texas at Arlington), Mert D. Pesé (Clemson University), Habeeb Olufowobi (University of Texas at Arlington)

Read More