Insvdf: Interface-State-Aware Virtual Device Fuzzing

Hypervisor is the core technology of virtualization for emulating independent hardware resources for each virtual machine. Virtual devices serve as the main interface of the hypervisor, making the security of virtual devices crucial, as any vulnerabilities can impact the entire virtualization environment and pose a threat to the host machine's security. Direct Memory Access (DMA) is the interface of virtual devices, enabling communication with the host machine. Recently, many efforts have focused on fuzzing against DMA to discover the hypervisor's vulnerabilities. However, the lack of sensitivity to the DMA state causes these efforts to be hindered in efficiency during fuzzing. Specifically, there are two main issues: the uncertain interaction moment and the unclear interaction depth. In this paper, we introduce InSVDF, a DMA interface stateaware fuzzing engine. InSVDF first models the intra-interface state of the DMA interface and incorporates an asynchronyaware state snapshot mechanism along with a depth-aware seed preservation mechanism. To validate our approach, we compare InSVDF with a state-of-the-art fuzzer. The results demonstrate that InSVDF significantly enhances vulnerability discovery speed, with improvements of up to 24.2 x in the best case. Furthermore, InSVDF has identified 2 new vulnerabilities, one of which has been assigned a CVE ID.

Authors

Institutions

Publication Details

Published
2025-04-26
DOI
https://doi.org/10.1109/icse55347.2025.00190
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Insvdf: Interface-State-Aware Virtual Device Fuzzing

Zulie Pan, Gaoning Pan, Zexiang Zhang, Ruipeng Wang et al.
Industrial Vision Systems and Defect Detection
article

Insvdf: Interface-State-Aware Virtual Device Fuzzing

Zulie Pan, Gaoning Pan, Zexiang Zhang, Ruipeng Wang, Cheng Tu, Yi Shen, Chunming Wu, Min Zhang, Yang Li, Yiming Tao
article en

Abstract

Hypervisor is the core technology of virtualization for emulating independent hardware resources for each virtual machine. Virtual devices serve as the main interface of the hypervisor, making the security of virtual devices crucial, as any vulnerabilities can impact the entire virtualization environment and pose a threat to the host machine's security. Direct Memory Access (DMA) is the interface of virtual devices, enabling communication with the host machine. Recently, many efforts have focused on fuzzing against DMA to discover the hypervisor's vulnerabilities. However, the lack of sensitivity to the DMA state causes these efforts to be hindered in efficiency during fuzzing. Specifically, there are two main issues: the uncertain interaction moment and the unclear interaction depth. In this paper, we introduce InSVDF, a DMA interface stateaware fuzzing engine. InSVDF first models the intra-interface state of the DMA interface and incorporates an asynchronyaware state snapshot mechanism along with a depth-aware seed preservation mechanism. To validate our approach, we compare InSVDF with a state-of-the-art fuzzer. The results demonstrate that InSVDF significantly enhances vulnerability discovery speed, with improvements of up to 24.2 x in the best case. Furthermore, InSVDF has identified 2 new vulnerabilities, one of which has been assigned a CVE ID.

National University of Defense Technology (CN), Hangzhou Dianzi University (CN), Zhejiang University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 80%
Industrial Vision Systems and Defect Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.