ReScan: A Middleware Framework for Realistic and Robust Black-box Web Application Scanning

Black-box web vulnerability scanners are invaluable for security researchers and practitioners. Despite recent approaches tackling some of the inherent limitations of scanners, many have not sufficiently evolved alongside web browsers and applications, and often lack the capabilities for handling the inherent challenges of navigating and interacting with modern web applications. Instead of building an alternative scanner that could naturally only incorporate a limited set of the wide range of vulnerability-finding capabilities offered by the multitude of existing scanners, in this paper we propose an entirely different strategy.

We present ReScan, a scanner-agnostic middleware framework that transparently enhances scanners’ capabilities by mediating their interaction with web applications in a realistic and robust manner, using an orchestrated, fully-fledged modern browser. In essence, our framework can be used in conjunction with any vulnerability scanner, thus allowing users to benefit from the capabilities of existing and future scanners. Our extensible and modular framework includes a collection of enhancement techniques that address limitations and obstacles commonly faced by state-ofthe-art scanners.

Our experimental evaluation demonstrates that despite the considerable (and expected) overhead introduced by a fully-fledged browser, our framework significantly improves the code coverage achieved by popular scanners (168% on average), resulting in a 66% and 161% increase in the number of reflected and stored XSS vulnerabilities detected, respectively.

This work was supported by the National Science Foundation under grants CNS-1934597, CNS-2211574, CNS2143363. Any opinions, findings, conclusions, or recommendations expressed herein are those of the authors, and do not necessarily reflect those of the NSF. This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 883275 (HEIR), No. 883540 (PUZZLE) and No. 101021659 (SENTINEL).