Atropos: Effective Fuzzing of Web Applications for Server-Side Vulnerabilities
Emre Güler, Sergej Schumilo, Moritz Schloegel, Nils Bars, Philipp Görz, Xinyi Xu, Cemal Kaygusuz, Thorsten Holz
摘要
Server-side web applications are still predominantly implemented in the PHP programming language. Even nowadays, PHP-based web applications are plagued by many different types of security vulnerabilities, ranging from SQL injection to file inclusion and remote code execution. Automated security testing methods typically focus on static analysis and taint analysis. These methods are highly dependent on accurate modeling of the PHP language and often suffer from (potentially many) false positive alerts. Interestingly, dynamic testing techniques such as fuzzing have not gained acceptance in web applications testing, even though they avoid these common pitfalls and were rapidly adopted in other domains, e. g., for testing native applications written in C/C++. In this paper, we present ATROPOS, a snapshot-based, feedback-driven fuzzing method tailored for PHP-based web applications. Our approach considers the challenges associated with web applications, such as maintaining session state and generating highly structured inputs. Moreover, we propose a feedback mechanism to automatically infer the key-value structure used by web applications. Combined with eight new bug oracles, each covering a common class of vulnerabilities in server-side web applications, ATROPOS is the first approach to fuzz web applications effectively and efficiently. Our evaluation shows that ATROPOS significantly outperforms the current state of the art in web application testing. In particular, it finds, on average, at least 32% more bugs, while not reporting a single false positive on different test suites. When analyzing real-world web applications, we identify seven previously unknown vulnerabilities that can be exploited even by unauthenticated users.
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引用它的顶会 Paper20
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它引用的顶会 Paper16
- Evaluating Fuzz TestingGeorge Klees, Andrew Ruef, Benji Cooper, Shiyi Wei 等CCS 2018 · 被引用 753 次
- Angora: Efficient Fuzzing by Principled SearchPeng Chen, Hao ChenS&P 2018 · 被引用 616 次
- QSYM : A Practical Concolic Execution Engine Tailored for Hybrid FuzzingInsu Yun, Sangho Lee, Meng Xu, Yeongjin Jang 等USENIX Security 2018 · 被引用 537 次
- REDQUEEN: Fuzzing with Input-to-State CorrespondenceCornelius Aschermann, Sergej Schumilo, Tim Blazytko, Robert Gawlik 等NDSS 2019 · 被引用 413 次
- NAUTILUS: Fishing for Deep Bugs with GrammarsCornelius Aschermann, Tommaso Frassetto, Thorsten Holz, Patrick Jauernig 等NDSS 2019 · 被引用 291 次
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