Secure Content Sniffing for Web Browsers, or How to Stop Papers from Reviewing Themselves

  • Authors:
  • Adam Barth;Juan Caballero;Dawn Song

  • Affiliations:
  • -;-;-

  • Venue:
  • SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Cross-site scripting defenses often focus on HTML documents, neglecting attacks involving the browser's content-sniffing algorithm, which can treat non-HTML content as HTML.Web applications, such as the one that manages this conference, must defend themselves against these attacks or risk authors uploading malicious papers that automatically submit stellar self-reviews.In this paper, we formulate content-sniffing XSS attacks and defenses.We study content-sniffing XSS attacks systematically by constructing high-fidelity models of the content-sniffing algorithms used by four major browsers.We compare these models with Web site content filtering policies to construct attacks.To defend against these attacks, we propose and implement a principled content-sniffing algorithm that provides security while maintaining compatibility.Our principles have been adopted, in part, by Internet Explorer 8 and, in full, by Google Chrome and the HTML 5 working group.