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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Automated Mass Malware Factory: The Convergence of Piggybacking and...

Heng Li (Huazhong University of Science and Technology), Zhiyuan Yao (Huazhong University of Science and Technology), Bang Wu (Huazhong University of Science and Technology), Cuiying Gao (Huazhong University of Science and Technology), Teng Xu (Huazhong University of Science and Technology), Wei Yuan (Huazhong University of Science and Technology), Xiapu Luo (The Hong Kong Polytechnic University)

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The Forking Way: When TEEs Meet Consensus

Annika Wilde (Ruhr University Bochum), Tim Niklas Gruel (Ruhr University Bochum), Claudio Soriente (NEC Laboratories Europe), Ghassan Karame (Ruhr University Bochum)

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Evaluating the Strength and Availability of Multilingual Passphrase Authentication

Chi-en Amy Tai (University of Waterloo), Urs Hengartner (University of Waterloo), Alexander Wong (University of Waterloo)

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MineShark: Cryptomining Traffic Detection at Scale

Shaoke Xi (Zhejiang University), Tianyi Fu (Zhejiang University), Kai Bu (Zhejiang University), Chunling Yang (Zhejiang University), Zhihua Chang (Zhejiang University), Wenzhi Chen (Zhejiang University), Zhou Ma (Zhejiang University), Chongjie Chen (HANG ZHOU CITY BRAIN CO., LTD), Yongsheng Shen (HANG ZHOU CITY BRAIN CO., LTD), Kui Ren (Zhejiang University)

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