Hide User Account From Sign-In Screen
This analytic identifies a suspicious registry modification to hide a user account on the Windows Login screen. This technique was seen in some tradecraft where the adversary will create a hidden user account with Admin privileges in login screen to avoid noticing by the user that they already compromise and to persist on that said machine.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2023-04-27
- Author: Steven Dick, Teoderick Contreras, Splunk
- ID: 834ba832-ad89-11eb-937d-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 | tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_data = "0x00000000") BY _time span=1h Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.registry_value_data Registry.process_guid | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `hide_user_account_from_sign_in_screen_filter`
The SPL above uses the following Macros:
hide_user_account_from_sign-in_screen_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
To successfully implement this search, you need to be ingesting logs with the registry value name, registry path, and registry value data from your endpoints. If you are using Sysmon, you must have at least version 2.0 of the offical Sysmon TA. https://splunkbase.splunk.com/app/5709
Known False Positives
Unknown. Filter as needed.
Associated Analytic Story
|72.0||90||80||Suspicious registry modification ($registry_value_name$) which is used go hide a user account on the Windows Login screen detected on $dest$ executed by $user$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
Replay any dataset to Splunk Enterprise by using our
replay.py tool or the UI.
Alternatively you can replay a dataset into a Splunk Attack Range
source | version: 4