NDLI: Model-based Security Testing: An Empirical Study on OAuth 2.0 Implementations

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Model-based Security Testing: An Empirical Study on OAuth 2.0 Implementations

Content Provider	ACM Digital Library
Author	Yang, Ronghai Li, Guanchen Hu, Pili Lau, Wing Cheong Zhang, Kehuan
Abstract	Motivated by the prevalence of OAuth-related vulnerabilities in the wild, large-scale security testing of real-world OAuth 2.0 implementations have received increasing attention lately [31,37,42]. However, these existing works either rely on manual discovery of new vulnerabilities in OAuth 2.0 implementations or perform automated testing for specific, previously-known vulnerabilities across a large number of OAuth implementations. In this work, we propose an adaptive model-based testing framework to perform automated, large-scale security assessments for OAuth 2.0 implementations in practice. Key advantages of our approach include (1) its ability to identify existing vulnerabilities and discover new ones in an automated manner; (2) improved testing coverage as all possible execution paths within the scope of the model will be checked and (3) its ability to cater for the implementation differences of practical OAuth systems/ applications, which enables the analyst to offload the manual efforts for large-scale testing of OAuth implementations. We have designed and implemented OAuthTester to realize our proposed framework. Using OAuthTester, we examine the implementations of 4 major Identity Providers as well as 500 top-ranked US and Chinese websites which use the OAuth-based Single-Sign-On service provided by the formers. Our empirical findings demonstrate the efficacy of adaptive model-based testing on OAuth 2.0 deployments at scale. More importantly, OAuthTester not only manages to rediscover various existing vulnerabilities but also identify several previously unknown security flaws and new exploits for a large number of eal-world applications implementing OAuth 2.0.
Starting Page	651
Ending Page	662
Page Count	12
File Format	PDF
ISBN	9781450342339
DOI	10.1145/2897845.2897874
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Single sign-on Security testing Oauth 2.0
Content Type	Text
Resource Type	Article

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