HDiff: A Semi-automatic Framework for Discovering Semantic Gap Attack in HTTP Implementations

Abstract

The Internet has become a complex distributed network with numerous middle-boxes, where an end-to-end HTTP request is often processed by multiple intermediate servers before it reaches its destination. However, a general problem in this distributed network is the extit{semantic gap attack}, which is defined as inconsistent semantic interpretations in the processing chain. While some studies have found individual semantic gap attacks, most of them are based on ad-hoc manual analysis, which is inadequate for fundamentally enhancing the security assurance of a system as complex as the HTTP network. In this work, we propose HDiff, a novel semi-automatic detecting framework, systematically exploring semantic gap attacks in HTTP implementations. We designed a documentation analyzer that employs natural language processing techniques to extract rules from specifications, and utilized differential testing to discover semantic gap attacks. We implemented and evaluated it to find three kinds of semantic gap attacks in 10 popular HTTP implementations. In total, HDiff found 14 vulnerabilities and 29 affected server pairs covering all three types of attacks. In particular, HDiff also discovered three new types of attack vectors. We have already duly reported all identified vulnerabilities to the involved HTTP software vendors and obtained 7 new CVEs from well-known HTTP software, including Apache, Tomcat, Weblogic, and Microsoft IIS Server.

Publication
In the 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Network, 2022

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