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

Deceptive Deletions for Protecting Withdrawn Posts on Social Media...

Mohsen Minaei (Visa Research), S Chandra Mouli (Purdue University), Mainack Mondal (IIT Kharagpur), Bruno Ribeiro (Purdue University), Aniket Kate (Purdue University)

Read More

WeepingCAN: A Stealthy CAN Bus-off Attack

Gedare Bloom (University of Colorado Colorado Springs) Best Paper Award Winner ($300 cash prize)!

Read More

Model-Agnostic Defense for Lane Detection against Adversarial Attack

Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

Read More