HyungSeok Han (KAIST), DongHyeon Oh (KAIST), Sang Kil Cha (KAIST)

JavaScript engines are an attractive target for attackers due to their popularity and flexibility in building exploits. Current state-of-the-art fuzzers for finding JavaScript engine vulnerabilities focus mainly on generating syntactically correct test cases based on either a predefined context-free grammar or a trained probabilistic language model. Unfortunately, syntactically correct JavaScript sentences are often semantically invalid at runtime. Furthermore, statically analyzing the semantics of JavaScript code is challenging due to its dynamic nature: JavaScript code is generated at runtime, and JavaScript expressions are dynamically-typed. To address this challenge, we propose a novel test case generation algorithm that we call semantics-aware assembly, and implement it in a fuzz testing tool termed CodeAlchemist. Our tool can generate arbitrary JavaScript code snippets that are both semantically and syntactically correct, and it effectively yields test cases that can crash JavaScript engines. We found numerous vulnerabilities of the latest JavaScript engines with CodeAlchemist and reported them to the vendors.

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rORAM: Efficient Range ORAM with O(log2 N) Locality

Anrin Chakraborti (Stony Brook University), Adam J. Aviv (United States Naval Academy), Seung Geol Choi (United States Naval Academy), Travis Mayberry (United States Naval Academy), Daniel S. Roche (United States Naval Academy), Radu Sion (Stony Brook University)

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Measurement and Analysis of Hajime, a Peer-to-peer IoT Botnet

Stephen Herwig (University of Maryland), Katura Harvey (University of Maryland, Max Planck Institute for Software Systems (MPI-SWS)), George Hughey (University of Maryland), Richard Roberts (University of Maryland, Max Planck Institute for Software Systems (MPI-SWS)), Dave Levin (University of Maryland)

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Anonymous Multi-Hop Locks for Blockchain Scalability and Interoperability

Giulio Malavolta (Friedrich-Alexander University Erlangen-Nürnberg), Pedro Moreno Sanchez (TU Wien), Clara Schneidewind (TU Wien), Aniket Kate (Purdue University), Matteo Maffei (TU Wien)

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Send Hardest Problems My Way: Probabilistic Path Prioritization for...

Lei Zhao (Wuhan University), Yue Duan (University of California, Riverside), Heng Yin (University of California, Riverside), Jifeng Xuan (Wuhan University)

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