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.

View More Papers

Practical Hidden Voice Attacks against Speech and Speaker Recognition...

Hadi Abdullah (University of Florida), Washington Garcia (University of Florida), Christian Peeters (University of Florida), Patrick Traynor (University of Florida), Kevin R. B. Butler (University of Florida), Joseph Wilson (University of Florida)

Read More

MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

Inken Hagestedt (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), Haixu Tang (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington), Michael Backes (CISPA Helmholtz Center for Information Security)

Read More

NAUTILUS: Fishing for Deep Bugs with Grammars

Cornelius Aschermann (Ruhr-Universität Bochum), Tommaso Frassetto (Technische Universität Darmstadt), Thorsten Holz (Ruhr-Universität Bochum), Patrick Jauernig (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technische Universität Darmstadt), Daniel Teuchert (Ruhr-Universität Bochum)

Read More

PeriScope: An Effective Probing and Fuzzing Framework for the...

Dokyung Song (University of California, Irvine), Felicitas Hetzelt (Technical University of Berlin), Dipanjan Das (University of California, Santa Barbara), Chad Spensky (University of California, Santa Barbara), Yeoul Na (University of California, Irvine), Stijn Volckaert (University of California, Irvine and KU Leuven), Giovanni Vigna (University of California, Santa Barbara), Christopher Kruegel (University of California, Santa Barbara),…

Read More