Manuel Andreas (Technical University of Munich), Fabian Specht (Technical University of Munich), Marius Momeu (Technical University of Munich)

Hypervisors are crucial for the security and availability of modern cloud infrastructures, yet they must expose a large virtualization interface to guest VMs—an attack surface that adversaries can exploit. Among the most intricate and security-sensitive components of hypervisors is their virtual CPU implementation, typically implemented at the highest privilege level. Although previous fuzzing research made promising steps towards scrutinizing the virtual CPU component of HVs, existing techniques fail at covering it in depth, as its convoluted nature requires laborious manual setup for accessing individual interfaces, all the while employing sub-optimal techniques that lower fuzzing throughput.

We address these shortcomings via HyperMirage, a novel hypervisor fuzzer that automatically and efficiently explores the large space of architectural states emulated by virtual CPU implementations. HyperMirage spares security analysts from manually crafting fuzzing seeds in the form of architecturally valid VM states by employing a novel Direct State Manipulation approach, which directly and automatically mutates the HV's view of a VM's state that is consumed during fuzzing. Additionally, we extend a state-of-the-art compiler-based symbolic execution engine, making it the first one available for bare-metal targets, and integrate it into an efficient coverage-guided HV fuzzer, enabling HyperMirage to drastically improve fuzzing throughput when compared to existing techniques.

We provide a case study of HyperMirage by fuzzing the production-grade Xen and KVM hypervisors on the Intel x86 architecture. Our evaluation shows that HyperMirage is capable of covering 200% more virtual CPU interfaces than prior work and achieves drastically more coverage on the entire virtual CPU space when compared to available HV fuzzers. Moreover, HyperMirage discovered 9 new bugs in Xen and 2 in KVM, all of which have been confirmed by the respective project maintainers.

View More Papers

FLIPPYRAM: A Large-Scale Study of Rowhammer Prevalence

Martin Heckel (Hof University of Applied Sciences), Nima Sayadi (Hof University of Applied Sciences), Jonas Juffinger (Graz University of Technology), Carina Fiedler (Graz University of Technology), Daniel Gruss (Graz University of Technology), Florian Adamsky (Hof University of Applied Sciences)

Read More

NEXUS: Towards Accurate and Scalable Mapping between Vulnerabilities and...

Ehsan Khodayarseresht (Concordia University), Suryadipta Majumdar (Concordia University), Serguei Mokhov (Concordia University), Mourad Debbabi (Concordia University)

Read More

Light2Lie: Detecting Deepfake Images Using Physical Reflectance Laws

Kavita Kumari (Technical University of Darmstadt), Sasha Behrouzi (Technical University of Darmstadt), Alessandro Pegoraro (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Read More