Email Addresses

Adversaries may gather email addresses that can be used during targeting. Even if internal instances exist, organizations may have public-facing email infrastructure and addresses for employees.

Adversaries may easily gather email addresses, since they may be readily available and exposed via online or other accessible data sets (ex: [Social Media](https://attack.mitre.org/techniques/T1593/001) or [Search Victim-Owned Websites](https://attack.mitre.org/techniques/T1594)).(Citation: HackersArise Email)(Citation: CNET Leaks) Email addresses could also be enumerated via more active means (i.e. [Active Scanning](https://attack.mitre.org/techniques/T1595)), such as probing and analyzing responses from authentication services that may reveal valid usernames in a system.(Citation: GrimBlog UsernameEnum) For example, adversaries may be able to enumerate email addresses in Office 365 environments by querying a variety of publicly available API endpoints, such as autodiscover and GetCredentialType.(Citation: GitHub Office 365 User Enumeration)(Citation: Azure Active Directory Reconnaisance)

Gathering this information may reveal opportunities for other forms of reconnaissance (ex: [Search Open Websites/Domains](https://attack.mitre.org/techniques/T1593) or [Phishing for Information](https://attack.mitre.org/techniques/T1598)), establishing operational resources (ex: [Email Accounts](https://attack.mitre.org/techniques/T1586/002)), and/or initial access (ex: [Phishing](https://attack.mitre.org/techniques/T1566) or [Brute Force](https://attack.mitre.org/techniques/T1110) via [External Remote Services](https://attack.mitre.org/techniques/T1133)).

Platforms: PRE
Tactics:
phase_name: reconnaissance

Detection:
Monitor for suspicious network traffic that could be indicative of probing for email addresses and/or usernames, such as large/iterative quantities of authentication requests originating from a single source (especially if the source is known to be associated with an adversary/botnet). Analyzing web metadata may also reveal artifacts that can be attributed to potentially malicious activity, such as referer or user-agent string HTTP/S fields.

Much of this activity may have a very high occurrence and associated false positive rate, as well as potentially taking place outside the visibility of the target organization, making detection difficult for defenders.

Detection efforts may be focused on related stages of the adversary lifecycle, such as during Initial Access.

References:
source_name: mitre-attack
description: Dr. Nestori Syynimaa. (2020, June 13). Just looking: Azure Active Directory reconnaissance as an outsider. Retrieved May 27, 2022.
source_name: Azure Active Directory Reconnaisance
description: gremwell. (2020, March 24). Office 365 User Enumeration. Retrieved May 27, 2022.
source_name: GitHub Office 365 User Enumeration
description: GrimHacker. (2017, July 24). Office365 ActiveSync Username Enumeration. Retrieved December 9, 2021.
source_name: GrimBlog UsernameEnum
description: Hackers Arise. (n.d.). Email Scraping and Maltego. Retrieved October 20, 2020.
source_name: HackersArise Email
description: Ng, A. (2019, January 17). Massive breach leaks 773 million email addresses, 21 million passwords. Retrieved October 20, 2020.
source_name: CNET Leaks