Samuel Groß (Google), Simon Koch (TU Braunschweig), Lukas Bernhard (Ruhr-University Bochum), Thorsten Holz (CISPA Helmholtz Center for Information Security), Martin Johns (TU Braunschweig)

JavaScript has become an essential part of the Internet infrastructure, and today's interactive web applications would be inconceivable without this programming language. On the downside, this interactivity implies that web applications rely on an ever-increasing amount of computationally intensive JavaScript code, which burdens the JavaScript engine responsible for efficiently executing the code. To meet these rising performance demands, modern JavaScript engines ship with sophisticated just-in-time (JIT) compilers. However, JIT compilers are a complex technology and, consequently, provide a broad attack surface for potential faults that might even be security-critical.
Previous work on discovering software faults in JavaScript engines found many vulnerabilities, often using fuzz testing. Unfortunately, these fuzzing approaches are not designed to generate source code that actually triggers JIT semantics. Consequently, JIT vulnerabilities are unlikely to be discovered by existing methods.
In this paper, we close this gap and present the first fuzzer that focuses on JIT vulnerabilities.
More specifically, we present the design and implementation of an intermediate representation (IR) that focuses on discovering JIT compiler vulnerabilities. We implemented a complete prototype of the proposed approach and evaluated our fuzzer over a period of six months. In total, we discovered 17 confirmed security vulnerabilities. Our results show that targeted JIT fuzzing is possible and a dangerously neglected gap in fuzzing coverage for JavaScript engines.

View More Papers

Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks...

Hadi Abdullah (Visa Research), Aditya Karlekar (University of Florida), Saurabh Prasad (University of Florida), Muhammad Sajidur Rahman (University of Florida), Logan Blue (University of Florida), Luke A. Bauer (University of Florida), Vincent Bindschaedler (University of Florida), Patrick Traynor (University of Florida)

Read More

Hope of Delivery: Extracting User Locations From Mobile Instant...

Theodor Schnitzler (Research Center Trustworthy Data Science and Security, TU Dortmund, and Ruhr-Universität Bochum), Katharina Kohls (Radboud University), Evangelos Bitsikas (Northeastern University and New York University Abu Dhabi), Christina Pöpper (New York University Abu Dhabi)

Read More

BARS: Local Robustness Certification for Deep Learning based Traffic...

Kai Wang (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Wenqi Chen (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University), Xia Yin (Tsinghua University)

Read More

Adventures in Wonderland: Automotive Cyber beyond the CAN Bus

Michael Westra (In-Vehicle Cyber Security Technical Manager, Ford)

Read More