Adversaries may gather information about the victim's identity that can be used during targeting. Information about identities may include a variety of details, including personal data (ex: employee names, email addresses, etc.) as well as sensitive details such as credentials.
Adversaries may gather this information in various ways, such as direct elicitation via Phishing for Information. Information about users could also be enumerated via other active means (i.e. Active Scanning) such as probing and analyzing responses from authentication services that may reveal valid usernames in a system.[1] Information about victims may also be exposed to adversaries via online or other accessible data sets (ex: Social Media or Search Victim-Owned Websites).[2][3][4][5][6][7][8][9]
Gathering this information may reveal opportunities for other forms of reconnaissance (ex: Search Open Websites/Domains or Phishing for Information), establishing operational resources (ex: Compromise Accounts), and/or initial access (ex: Phishing or Valid Accounts).
| ID | Name | Description |
|---|---|---|
| G0050 | APT32 |
APT32 has conducted targeted surveillance against activists and bloggers.[10] |
| G0059 | Magic Hound |
Magic Hound has acquired mobile phone numbers of potential targets, possibly for mobile malware or additional phishing operations.[11] |
| ID | Mitigation | Description |
|---|---|---|
| M1056 | Pre-compromise |
This technique cannot be easily mitigated with preventive controls since it is based on behaviors performed outside of the scope of enterprise defenses and controls. Efforts should focus on minimizing the amount and sensitivity of data available to external parties. |
| ID | Data Source | Data Component |
|---|---|---|
| DS0029 | Network Traffic | Network Traffic Content |
Monitor for suspicious network traffic that could be indicative of probing for user information, 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.