Name:Windows Set Account Password Policy To Unlimited Via Net id:11f93009-8083-43fd-82a7-821fcbdc8342 version:8 date:None author:Teoderick Contreras, Nasreddine Bencherchali, Splunk status:production type:Anomaly Description:The following analytic detects the use of net.exe or net1.exe to configure the Windows maximum password age policy as unlimited.
It leverages process creation telemetry from Endpoint Detection and Response (EDR) agents and identifies executions of the `net accounts` command using the `/maxpwage:unlimited` option.
When specified, Windows configures the maximum password age with an unlimited value, effectively preventing passwords governed by the affected password policy from expiring.
This behavior is significant because an adversary may modify password expiration settings to weaken credential security controls and maintain access to compromised accounts for an extended period.
If confirmed malicious, this activity may indicate an attempt to establish or maintain persistence by preventing expected password rotation from invalidating credentials under the affected password policy. Data_source:
-Sysmon EventID 1
-Windows Event Log Security 4688
-CrowdStrike ProcessRollup2
search:| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime
FROM datamodel=Endpoint.Processes WHERE
`process_net` Processes.process="* accounts *" Processes.process IN ("* /maxpwage:unlimited*")
how_to_implement:The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the `Processes` node of the `Endpoint` data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process. known_false_positives:This behavior is not commonly seen in production environment and not advisable, investigate the activity and determine if it is malicious. Apply additional filters to reduce false positives as needed. References: -https://app.any.run/tasks/a6f2ffe2-e6e2-4396-ae2e-04ea0143f2d8/ -https://docs.microsoft.com/en-us/troubleshoot/windows-server/networking/net-commands-on-operating-systems drilldown_searches: name:'View the detection results for - "$dest$"' search:'%original_detection_search% | search dest = "$dest$"' earliest_offset:'$info_min_time$' latest_offset:'$info_max_time$' name:'View risk events for the last 7 days for - "$dest$"' search:'| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' earliest_offset:'7d' latest_offset:'0' analytic_story:['Ransomware', 'BlackByte Ransomware', 'Crypto Stealer', 'XMRig']
