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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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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One Engine To Serve 'em All: Inferring Taint Rules...

Zheng Leong Chua (National University of Singapore), Yanhao Wang (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences), Teodora Baluta (National University of Singapore), Prateek Saxena (National University of Singapore), Zhenkai Liang (National University of Singapore), Purui Su (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences)

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Statistical Privacy for Streaming Traffic

Xiaokuan Zhang (The Ohio State University), Jihun Hamm (The Ohio State University), Michael K. Reiter (University of North Carolina at Chapel Hill), Yinqian Zhang (The Ohio State University)

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ML-Leaks: Model and Data Independent Membership Inference Attacks and...

Ahmed Salem (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security), Mathias Humbert (Swiss Data Science Center, ETH Zurich/EPFL), Pascal Berrang (CISPA Helmholtz Center for Information Security), Mario Fritz (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security)

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