Hwanjo Heo (ETRI), Seungwon Woo (ETRI/KAIST), Taeung Yoon (KAIST), Min Suk Kang (KAIST), Seungwon Shin (KAIST)

We present a practical partitioning attack, which we call Gethlighting, that isolates an Ethereum full node from the rest of the network for hours without having to occupy (or eclipse) all of the target’s peer connections. In Gethlighting, an adversary controls only about a half (e.g., 25 out of total 50) of all peer connections of a target node, achieving powerful partitioning with a small attack budget of operating several inexpensive virtual machines. At the core of Gethlighting, its low-rate denial-of-service (DoS) strategy effectively stops the growth of local blockchain for hours while leaving other Ethereum node operations undisturbed. We analyze how subtle and insignificant delays incurred by a low-rate DoS can lead to a powerful blockchain partitioning attack. The practical impact of Gethlighting is discussed — i.e., the attack is scalable and low-cost (only about $5,714 for targeting all Ethereum full nodes concurrently for 24 hours), and extremely simple to launch. We demonstrate the feasibility of Gethlighting with full nodes in the Ethereum mainnet and testnet in both controlled and real-world experiments. We identify a number of fundamental system characteristics in Ethereum that enable Gethlighting attacks and propose countermeasures that require some protocol and client implementation enhancements. Ethereum Foundation has acknowledged this vulnerability in September 2022 and one of our countermeasures has been accepted as a hotfix for Geth 1.11.0.

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Baltasar Dinis (Instituto Superior Técnico (IST-ULisboa) / INESC-ID / MPI-SWS), Peter Druschel (MPI-SWS), Rodrigo Rodrigues (Instituto Superior Técnico (IST-ULisboa) / INESC-ID)

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Jinghan Yang, Andew Estornell, Yevgeniy Vorobeychik (Washington University in St. Louis)

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Xiang Li (Tsinghua University), Baojun Liu (Tsinghua University), Xuesong Bai (University of California, Irvine), Mingming Zhang (Tsinghua University), Qifan Zhang (University of California, Irvine), Zhou Li (University of California, Irvine), Haixin Duan (Tsinghua University; QI-ANXIN Technology Research Institute; Zhongguancun Laboratory), Qi Li (Tsinghua University; Zhongguancun Laboratory)

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OptRand: Optimistically Responsive Reconfigurable Distributed Randomness

Adithya Bhat (Purdue University), Nibesh Shrestha (Rochester Institute of Technology), Aniket Kate (Purdue University), Kartik Nayak (Duke 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)