Name:Potential Telegram API Request Via CommandLine id:d6b0d627-d0bf-46b1-936f-c48284767d21 version:1 date:2025-02-19 author:Nasreddine Bencherchali, Splunk, Zaki Zarkasih Al Mustafa status:production type:Anomaly Description:The following analytic detects the presence of "api.telegram.org" in the CommandLine of a process. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity can be significant as the telegram API has been used as an exfiltration mechanism or even as a C2 channel. If confirmed malicious, this could allow an attacker or malware to exfiltrate data or receive additional C2 instruction, potentially leading to further compromise and persistence within the network. 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.process= "*api.telegram.org*" NOT Processes.process IN ("*-osint -url*", "* --single-argument*") by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process_id Processes.process_name Processes.process Processes.process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `potential_telegram_api_request_via_commandline_filter`
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:False positive may stem from application or users requesting the API directly via CommandLine for testing purposes. Investigate the matches and apply the necessary filters. References: -https://www.virustotal.com/gui/file/0b3ef5e04329cefb5bb4bf30b3edcb32d1ec6bbcb29d22695a079bfb5b56e8ac/behavior -https://www.virustotal.com/gui/file/72c59eeb15b5ec1d95e72e4b06a030bc058822bc10e5cb807e78a4624d329666/behavior -https://www.virustotal.com/gui/file/72c59eeb15b5ec1d95e72e4b06a030bc058822bc10e5cb807e78a4624d329666/content -https://www.virustotal.com/gui/file/1c4541bf70b6e251ef024ec4dde8dce400539c2368461c0d90e15a81b11ace44/content 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 ("$dest$") starthoursago=168 | 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:'$info_min_time$' latest_offset:'$info_max_time$' tags: analytic_story: - 'XMRig' asset_type:Endpoint mitre_attack_id: - 'T1102.002' - 'T1041' product: - 'Splunk Enterprise' - 'Splunk Enterprise Security' - 'Splunk Cloud' security_domain:endpoint
