For over a decade, phishing toolkits have been helping attackers automate and streamline their phishing campaigns. Man-in-the- Middle (MITM) phishing toolkits are the latest evolution in this space, where toolkits act as malicious reverse proxy servers of online services, mirroring live content to users while extracting cre- dentials and session cookies in transit. These tools further reduce the work required by attackers, automate the harvesting of 2FA- authenticated sessions, and substantially increase the believability of phishing web pages. In this paper, we present the first analysis of MITM phishing toolkits used in the wild. By analyzing and experimenting with these toolkits, we identify intrinsic network-level properties that can be used to identify them. Based on these properties, we develop a machine learning classifier that identifies the presence of such toolkits in online communications with 99.9% accuracy. We conduct a large-scale longitudinal study of MITM phishing toolkits by creating a data-collection framework that monitors and crawls suspicious URLs from public sources. Using this infrastruc- ture, we capture data on 1,220 MITM phishing websites over the course of a year. We discover that MITM phishing toolkits occupy a blind spot in phishing blocklists, with only 43.7% of domains and 18.9% of IP addresses associated with MITM phishing toolkits present on blocklists, leaving unsuspecting users vulnerable to these attacks. Our results show that our detection scheme is resilient to the cloaking mechanisms incorporated by these tools, and is able to detect previously hidden phishing content. Finally, we propose methods that online services can utilize to fingerprint requests origi- nating from these toolkits and stop phishing attempts as they occur.