Name:Attacker Tools On Endpoint id:a51bfe1a-94f0-48cc-b4e4-16a110145893 version:19 date:None author:Bhavin Patel, Splunk, sventec, Github Community status:production type:TTP Description:The following analytic detects the execution of tools commonly exploited by cybercriminals, such as those used for unauthorized access, network scanning, privilege escalation, password dumping or data exfiltration.
It leverages process activity data from Endpoint Detection and Response (EDR) agents, focusing on known attacker tool names.
This activity is significant because it serves as an early warning system for potential security incidents, enabling prompt response. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further network compromise, posing a severe threat to the organization's security infrastructure. Data_source:
-Sysmon EventID 1
-Windows Event Log Security 4688
-CrowdStrike ProcessRollup2
-Cisco Network Visibility Module Flow Data
search:| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process FROM datamodel=Endpoint.Processes WHERE [
| inputlookup attacker_tools
| rename attacker_tool_names AS Processes.process_name
| fields Processes.process_name ] AND Processes.dest!=unknown Processes.user!=unknown
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:Some administrator activity can be potentially triggered, please add those users to the filter macro. References: drilldown_searches: name:'View the detection results for - "$user$" and "$dest$"' search:'%original_detection_search% | search user = "$user$" dest = "$dest$"' earliest_offset:'$info_min_time$' latest_offset:'$info_max_time$' name:'View risk events for the last 7 days for - "$user$" and "$dest$"' search:'| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") | 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:['XMRig', 'Unusual Processes', 'SamSam Ransomware', 'CISA AA22-264A', 'Compromised Windows Host', 'PHP-CGI RCE Attack on Japanese Organizations', 'Cisco Network Visibility Module Analytics', 'Scattered Spider']
