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

The Advantages of Distributed TCAM Firewalls in Automotive Real-Time...

Evan Allen (Virginia Tech), Zeb Bowden (Virginia Tech Transportation Institute), J. Scot Ransbottom (Virginia Tech)

Read More

WIP: An Adaptive High Frequency Removal Attack to Bypass...

Yuki Hayakawa (Keio University), Takami Sato (University of California, Irvine), Ryo Suzuki, Kazuma Ikeda, Ozora Sako, Rokuto Nagata (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

Read More

Using Behavior Monitoring to Identify Privacy Concerns in Smarthome...

Atheer Almogbil, Momo Steele, Sofia Belikovetsky (Johns Hopkins University), Adil Inam (University of Illinois at Urbana-Champaign), Olivia Wu (Johns Hopkins University), Aviel Rubin (Johns Hopkins University), Adam Bates (University of Illinois at Urbana-Champaign)

Read More