Xigao Li (Stony Brook University), Anurag Yepuri (Stony Brook University), Nick Nikiforakis (Stony Brook University)

As cryptocurrencies increase in popularity and users obtain and manage their own assets, attackers are pivoting from just abusing cryptocurrencies as a payment mechanism, to stealing crypto assets from end users. In this paper, we report on the first large-scale analysis of cryptocurrency giveaway scams. Giveaway scams are deceptively simple scams where attackers set up webpages advertising fake events and promising users to double or triple the funds that they send to a specific wallet address. To understand the population of these scams in the wild we design and implement CryptoScamTracker, a tool that uses Certificate Transparency logs to identify likely giveaway scams. Through a 6-month-long experiment, CryptoScamTracker identified a total of 10,079 giveaway scam websites targeting users of all popular cryptocurrencies. Next to analyzing the hosting and domain preferences of giveaway scammers, we perform the first quantitative analysis of stolen funds using the public blockchains of the abused cryptocurrencies, extracting the transactions corresponding to 2,266 wallets belonging to scammers. We find that just for the scams discovered in our reporting period, attackers have stolen the equivalent of tens of millions of dollars, organizing large-scale campaigns across different cryptocurrencies. Lastly, we find evidence that attackers try to re-victimize users by offering fund-recovery services and that some victims send funds multiple times to the same scammers.

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RR: A Fault Model for Efficient TEE Replication

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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Browser Permission Mechanisms Demystified

Kazuki Nomoto (Waseda University), Takuya Watanabe (NTT Social Informatics Laboratories), Eitaro Shioji (NTT Social Informatics Laboratories), Mitsuaki Akiyama (NTT Social Informatics Laboratories), Tatsuya Mori (Waseda University/NICT/RIKEN AIP)

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WIP: Augmenting Vehicle Safety With Passive BLE

Noah T. Curran (University of Michigan), Kang G. Shin (University of Michigan), William Hass (Lear Corporation), Lars Wolleschensky (Lear Corporation), Rekha Singoria (Lear Corporation), Isaac Snellgrove (Lear Corporation), Ran Tao (Lear Corporation)

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