Name:Windows DLL Side-Loading Process Child Of Calc id:295ca9ed-e97b-4520-90f7-dfb6469902e1 version:13 date:None author:Teoderick Contreras, Splunk status:production type:Anomaly Description:The following analytic identifies suspicious child processes spawned by calc.exe, indicative of a potential DLL side-loading technique. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process GUIDs, names, and parent processes. In previous versions of the "calc.exe" binary, namely on Windows 7, it was vulnerable to DLL side-loading, where an attacker is able to load an arbitrary DLL named "WindowsCodecs.dll". This activity was observed in Qakbot malware, back in 2022. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, maintain persistence, and escalate privileges, posing a severe threat to the environment. Data_source:
-Sysmon EventID 1
-Windows Event Log Security 4688
-CrowdStrike ProcessRollup2
search:| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "calc.exe" Processes.process_name != "win32calc.exe"
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:No false positives have been identified at this time. References: -https://malpedia.caad.fkie.fraunhofer.de/details/win.qakbot -https://www.menlosecurity.com/blog/an-anatomy-of-heat-attacks-used-by-qakbot-campaigns 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:['Qakbot', 'Earth Alux']
