Connor Glosner (Purdue University), Aravind Machiry (Purdue University)

Unified Extensible Firmware Interface (UEFI) specification describes a platform-independent pre-boot interface for an Operating System (OS). EDK-2 Vulnerabilities in UEFI interface functions have severe consequences and can lead to Bootkits and other persistent malware resilient to OS reinstallations. However, there exist no vulnerability detection techniques for UEFI interfaces. We present FUZZUER, a feedback-guided fuzzing technique for UEFI interfaces on EDK-2, an exemplary and prevalently used UEFI implementation. We designed FIRNESS that utilizes static analysis techniques to automatically generate fuzzing harnesses for interface functions. We evaluated FUZZUER on the latest version of EDK-2. Our comprehensive evaluation on 150 interface functions demonstrates that FUZZUER with FIRNESS is an effective testing technique of EDK-2’s UEFI interface functions, greatly outperforming HBFA, an existing testing tool with manually written harnesses. We found 20 new security vulnerabilities, and most of these are already acknowledged by the developers.

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Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing

Ruyi Ding (Northeastern University), Tong Zhou (Northeastern University), Lili Su (Northeastern University), Aidong Adam Ding (Northeastern University), Xiaolin Xu (Northeastern University), Yunsi Fei (Northeastern University)

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Power-Related Side-Channel Attacks using the Android Sensor Framework

Mathias Oberhuber (Graz University of Technology), Martin Unterguggenberger (Graz University of Technology), Lukas Maar (Graz University of Technology), Andreas Kogler (Graz University of Technology), Stefan Mangard (Graz University of Technology)

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BARBIE: Robust Backdoor Detection Based on Latent Separability

Hanlei Zhang (Zhejiang University), Yijie Bai (Zhejiang University), Yanjiao Chen (Zhejiang University), Zhongming Ma (Zhejiang University), Wenyuan Xu (Zhejiang University)

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URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

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