The following analytic identifies a modification in the Windows registry to disable Windows error reporting settings. This Windows feature allows the user to report bugs, errors, failure or problems encountered in specific application or processes. Adversaries use this technique to hide any error or failure that some of its malicious components trigger.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-06-22
- Author: Teoderick Contreras, Splunk
- ID: 21cbcaf1-b51f-496d-a0c1-858ff3070452
Kill Chain Phase
- CIS 3
- CIS 5
- CIS 16
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\Windows Error Reporting\\disable*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disabling_wer_settings_filter`
The SPL above uses the following Macros:
windows_modify_registry_disabling_wer_settings_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 information on process that include the name of the process responsible for the changes from your endpoints into the
Endpoint datamodel in the
Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
Known False Positives
Administrators may enable or disable this feature that may cause some false positive, however is not common. Filter as needed.
Associated Analytic Story
|49.0||70||70||the registry for WER settings was modified to be disabled on $dest$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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