(2021) Black Widow: Blackbox Data-driven Web Scanning.
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Abstract
Modern web applications are an integral part of our digital lives. As we put more trust in web applications, the need for security increases. At the same time, detecting vulnerabilities in web applications has become increasingly hard, due to the complexity, dynamism, and reliance on third-party components. Blackbox vulnerability scanning is especially challenging because (i) for deep penetration of web applications scanners need to exercise such browsing behavior as user interaction and asynchrony, and (ii) for detection of nontrivial injection attacks, such as stored cross-site scripting (XSS), scanners need to discover inter-page data dependencies. This paper illuminates key challenges for crawling and scanning the modern web. Based on these challenges we identify three core pillars for deep crawling and scanning: navigation modeling, traversing, and tracking inter-state dependencies. While prior efforts are largely limited to the separate pillars, we suggest an approach that leverages all three. We develop Black Widow, a blackbox data-driven approach to web crawling and scanning. We demonstrate the effectiveness of the crawling by code coverage improvements ranging from 63% to 280% compared to other crawlers across all applications. Further, we demonstrate the effectiveness of the web vulnerability scanning by featuring no false positives and finding more cross-site scripting vulnerabilities than previous methods. In older applications, used in previous research, we find vulnerabilities that the other methods miss. We also find new vulnerabilities in production software, including HotCRP, osCommerce, PrestaShop and WordPress.
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
|---|---|
| Divisions: | Giancarlo Pellegrino (GP) |
| Conference: | SP IEEE Symposium on Security and Privacy |
| Depositing User: | Giancarlo Pellegrino |
| Date Deposited: | 25 Sep 2020 08:53 |
| Last Modified: | 15 Oct 2020 16:29 |
| Primary Research Area: | NRA5: Empirical & Behavioral Security |
| URI: | https://publications.cispa.saarland/id/eprint/3224 |
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