Sarisht Wadhwa (Duke University), Jannis Stoeter (Duke University), Fan Zhang (Duke University, Yale University), Kartik Nayak (Duke University)

Hashed Time-Locked Contracts (HTLCs) are a widely used primitive in blockchain systems such as payment channels, atomic swaps, etc. Unfortunately, HTLC is incentive-incompatible and is vulnerable to bribery attacks. The state-of-the-art solution is MAD-HTLC (Oakland'21), which proposes an elegant idea that leverages miners' profit-driven nature to defeat bribery attacks.

In this paper, we show that MAD-HTLC is still vulnerable as it only considers a somewhat narrow set of passive strategies by miners. Through a family of novel reverse-bribery attacks, we show concrete active strategies that miners can take to break MAD-HTLC and profit at the loss of MAD-HTLC users. For these attacks, we present their implementation and game-theoretical profitability analysis.

Based on the learnings from our attacks, we propose a new HTLC realization, He-HTLC (Our specification is lightweight and inert to incentive manipulation attacks. Hence, we call it He-HTLC where He stands for Helium.) that is provably secure against all possible strategic manipulation (passive and active). In addition to being secure in a stronger adversary model, He-HTLC achieves other desirable features such as low and user-adjustable collateral, making it more practical to implement and use the proposed schemes. We implemented He-HTLC on Bitcoin and the transaction cost of He-HTLC is comparative to average Bitcoin transaction fees.

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Patrick Jauernig (Technical University of Darmstadt), Domagoj Jakobovic (University of Zagreb, Croatia), Stjepan Picek (Radboud University and TU Delft), Emmanuel Stapf (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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Haohuang Wen (Ohio State University), Phillip Porras (SRI International), Vinod Yegneswaran (SRI International), Zhiqiang Lin (Ohio State University)

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Yan Shoshitaishvili (Arizona State University)

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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)