AI

Content Sniffing with Comma Chameleon

Abstract

MIME type sniffing or content sniffing has led to a new class of web security problems closely related to polyglots: if one partially controls the server response in, e.g., an API call response or a returned document and convinces the browser to treat this response as HTML, then it’s straightforward XSS. The attacker would be able to impersonate the user in the context of the given domain: if it is hosting a web application, an exploit would be able to read user data and perform arbitrary actions in the name of the user in the given web application. In other cases, user content might be interpreted as other (non-HTML) types, and then, instead of XSS, content-sniffing vulnerabilities would be permitted for the exfiltration of cross-domain data— just as bad.

We focus on PDF-based content-sniffing attacks. Our goal is to construct a payload that turns a harmless content injection into passive file formats (e.g., JSON or CSV) into an XSS-equivalent content sniffing vulnerability.