Shubham Agarwal (Saarland University), Ben Stock (CISPA Helmholtz Center for Information Security)

[NOTE: The authors of this paper found critical errors in their methodology after it was presented and published at the workshop and asked to withdraw the paper from the proceedings. As such, in the current version, we mark the paper as incorrect to help future research not repeating the same mistakes. We hope the authors will repeat their measurements with a fixed approach in future.]

Browser extensions are add-ons that aim to enhance the functionality of native Web applications on the client side. They intend to provide a rich end-user experience by leveraging feature-rich privileged JavaScript APIs, otherwise inaccessible for native applications. However, numerous large-scale investigations have also reported that extensions often indulge in malicious activities by exploiting access to these privileged APIs such as ad injection, stealing privacy-sensitive data, user fingerprinting, spying user activities on the Web, and malware distribution. In this work, we instead focus on tampering with security headers. To that end, we analyze over 186K Chrome extensions, publicly available on the Chrome Web Store, to detect extensions that actively intercept requests and responses and tamper with their security headers by either injecting, dropping, or modifying them, thereby undermining the security guarantees that these headers typically provide. We propose an automated framework to detect such extensions by leveraging a combination of static and dynamic analysis techniques. We evaluate our proposed methodology by investigating the extensions’ behavior against Tranco Top 100 domains and domains targeted explicitly by the extensions under test and report our findings. We observe that over 2.4K extensions actively tamper with at least one security header, undermining the purpose of the server-delivered, client-enforced security headers.

View More Papers

Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing

Jinghan Wang (University of California, Riverside), Chengyu Song (University of California, Riverside), Heng Yin (University of California, Riverside)

Read More

Data Poisoning Attacks to Deep Learning Based Recommender Systems

Hai Huang (Tsinghua University), Jiaming Mu (Tsinghua University), Neil Zhenqiang Gong (Duke University), Qi Li (Tsinghua University), Bin Liu (West Virginia University), Mingwei Xu (Tsinghua University)

Read More

Does Every Second Count? Time-based Evolution of Malware Behavior...

Alexander Küchler (Fraunhofer AISEC), Alessandro Mantovani (EURECOM), Yufei Han (NortonLifeLock Research Group), Leyla Bilge (NortonLifeLock Research Group), Davide Balzarotti (EURECOM)

Read More

V2X Security: Status and Open Challenges

Jonathan Petit (Director Of Engineering at Qualcomm Technologies) Dr. Jonathan Petit is Director of Engineering at Qualcomm Technologies, Inc., where he leads research in security of connected and automated vehicles (CAV). His team works on designing security solutions, but also develops tools for automotive penetration testing and builds prototypes. His recent work on misbehavior protection…

Read More