Sergej Schumilo (Ruhr-Universität Bochum), Cornelius Aschermann (Ruhr-Universität Bochum), Ali Abbasi (Ruhr-Universität Bochum), Simon Wörner (Ruhr-Universität Bochum), Thorsten Holz (Ruhr-Universität Bochum)

Applying modern fuzzers to novel targets is often a very lucrative venture. Hypervisors are part of a very critical code base: compromising them could allow an attacker to compromise the whole cloud infrastructure of any cloud provider. In this paper, we build a novel fuzzer that aims explicitly at testing modern hypervisors.

Our high throughput fuzzer design for long running interactive targets allows us to fuzz a large number of hypervisors, both open source, and proprietary. In contrast to one-dimensional fuzzers such as AFL, HYPER-CUBE can interact with any number of interfaces in any order.

Our evaluation shows that we can find more bugs (over 2x) and coverage (as much as 2x) than state of the art hypervisor fuzzers. Additionally, in most cases, we were able to do so using multiple orders of magnitude less time than comparable fuzzers. HYPER-CUBE was also able to rediscover a set of well-known vulnerabilities for hypervisors, such as VENOM, in less than five minutes. In total, HYPER-CUBE found 54 novel bugs, and so far we obtained 37 CVEs.

Our evaluation results demonstrates that next generation coverage-guided fuzzers should incorporate a higher-throughput design for long running targets such as hypervisors.

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Finding Safety in Numbers with Secure Allegation Escrows

Venkat Arun (Massachusetts Institute of Technology), Aniket Kate (Purdue University), Deepak Garg (Max Planck Institute for Software Systems), Peter Druschel (Max Planck Institute for Software Systems), Bobby Bhattacharjee (University of Maryland)

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SymTCP: Eluding Stateful Deep Packet Inspection with Automated Discrepancy...

Zhongjie Wang (University of California, Riverside), Shitong Zhu (University of California, Riverside), Yue Cao (University of California, Riverside), Zhiyun Qian (University of California, Riverside), Chengyu Song (University of California, Riverside), Srikanth V. Krishnamurthy (University of California, Riverside), Kevin S. Chan (U.S. Army Research Lab), Tracy D. Braun (U.S. Army Research Lab)

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CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples

Honggang Yu (University of Florida), Kaichen Yang (University of Florida), Teng Zhang (University of Central Florida), Yun-Yun Tsai (National Tsing Hua University), Tsung-Yi Ho (National Tsing Hua University), Yier Jin (University of Florida)

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Poseidon: Mitigating Volumetric DDoS Attacks with Programmable Switches

Menghao Zhang (Tsinghua University), Guanyu Li (Tsinghua University), Shicheng Wang (Tsinghua University), Chang Liu (Tsinghua University), Ang Chen (Rice University), Hongxin Hu (Clemson University), Guofei Gu (Texas A&M University), Qi Li (Tsinghua University), Mingwei Xu (Tsinghua University), Jianping Wu (Tsinghua University)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)