Web Application Firewalls (WAFs) have been introduced as essential and popular security gates that inspect incoming HTTP traffic to filter out malicious requests and provide defenses against a diverse array of web-based threats. Evading WAFs can compromise these defenses, potentially harming Internet users. In recent years, parsing discrepancies have plagued many entities in the communication path; however, their potential impact on WAF evasion and request smuggling remains largely unexplored. In this work, we present an innovative approach to bypassing WAFs by uncovering and exploiting parsing discrepancies through advanced fuzzing techniques. By targeting non-malicious components such as headers and segments of the body and using widely used content-types such as application/ json, multipart/form-data, and application/xml, we identified and confirmed 1207 bypasses across 5 well-known WAFs, AWS, Azure, Cloud Armor, Cloudflare, and Mod-Security. To validate our findings, we conducted a study in the wild, revealing that more than 90 % of websites ac-cepted both application/x-www-form-urlencoded and multipart/form-data interchangeably, highlighting a significant vulnerability and the broad applicability of our bypass techniques. We have reported these vulnerabilities to the affected parties and received acknowledgments from all, as well as bug bounty rewards from some vendors. Further, to mitigate these vulnerabilities, we introduce HTTP-Normalizer, a robust proxy tool designed to rigorously validate HTTP requests against current RFC standards. Our results demonstrate its effectiveness in normalizing or blocking all bypass attempts presented in this work.

Modern web applications involve multiple HTTP processors on the traffic path, each acting as a reverse proxy and processing client requests. Even when such proxies are secure in isolation, when combined into complex systems, minor HTTP parsing discrepancies between them can lead to various severe attacks such as cache poisoning and HTTP request smuggling attacks.

Internet-based media and social networks enable quick access to information; however, that has also made it easy to conduct disinformation campaigns. Fake news poses a serious threat to the functioning and safety of our society, as demonstrated by nation-state-sponsored campaigns to sway the 2016 US presidential election, and more recently COVID-19 pandemic hoaxes that promote false cures, putting lives at risk.

HTTP Request Smuggling (HRS) is an attack that exploits the HTTP processing discrepancies between two servers deployed in a proxy-origin configuration, allowing attackers to smuggle hidden requests through the proxy. While this idea is not new, HRS is soaring in popularity due to recently revealed novel exploitation techniques and real-life abuse scenarios. In this work, we step back from the highly-specific exploits hogging the spotlight, and present the first work that systematically explores HRS within a scientific framework. We design an experiment infrastructure powered by a novel grammar-based differential fuzzer, test 10 popular server/proxy/CDN technologies in combinations, identify pairs that result in processing discrepancies, and discover exploits that lead to HRS. Our experiment reveals previously unknown ways to manipulate HTTP requests for exploitation, and for the first time documents the server pairs prone to HRS.