splunk_escuAnomaly

Gsuite Email With Known Abuse Web Service Link

The following analytic detects emails in Gsuite containing links to known abuse web services such as Pastebin, Telegram, and Discord. It leverages Gsuite Gmail logs to identify emails with these specific domains in their links. This activity is significant because these services are commonly used by attackers to deliver malicious payloads. If confirmed malicious, this could lead to the delivery of malware, phishing attacks, or other harmful activities, potentially compromising sensitive information or systems within the organization.

MITRE ATT&CK

T1566.001

Detection Query

`gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me")
  | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)"
  | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)"
  | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com"
  | eval phase="plan"
  | eval severity="low"
  | stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time) as lastTime count
    BY is_spam source.address source.from_header_address
       subject destination{}.address phase
       severity
  | `security_content_ctime(firstTime)`
  | `security_content_ctime(lastTime)`
  | `gsuite_email_with_known_abuse_web_service_link_filter`

Author

Teoderick Contreras, Splunk

Created

2026-03-10

Data Sources

G Suite Gmail

References

https://news.sophos.com/en-us/2021/07/22/malware-increasingly-targets-discord-for-abuse/

Tags

Dev Sec Ops

Raw Content

name: Gsuite Email With Known Abuse Web Service Link
id: 8630aa22-042b-11ec-af39-acde48001122
version: 8
date: '2026-03-10'
author: Teoderick Contreras, Splunk
status: production
type: Anomaly
description: The following analytic detects emails in Gsuite containing links to known abuse web services such as Pastebin, Telegram, and Discord. It leverages Gsuite Gmail logs to identify emails with these specific domains in their links. This activity is significant because these services are commonly used by attackers to deliver malicious payloads. If confirmed malicious, this could lead to the delivery of malware, phishing attacks, or other harmful activities, potentially compromising sensitive information or systems within the organization.
data_source:
    - G Suite Gmail
search: |-
    `gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me")
      | rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)"
      | rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)"
      | where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com"
      | eval phase="plan"
      | eval severity="low"
      | stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time) as lastTime count
        BY is_spam source.address source.from_header_address
           subject destination{}.address phase
           severity
      | `security_content_ctime(firstTime)`
      | `security_content_ctime(lastTime)`
      | `gsuite_email_with_known_abuse_web_service_link_filter`
how_to_implement: To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.
known_false_positives: normal email contains this link that are known application within the organization or network can be catched by this detection.
references:
    - https://news.sophos.com/en-us/2021/07/22/malware-increasingly-targets-discord-for-abuse/
drilldown_searches:
    - name: View the detection results for - "$destination{}.address$"
      search: '%original_detection_search% | search  destination{}.address = "$destination{}.address$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$destination{}.address$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$destination{}.address$") 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: $info_max_time$
rba:
    message: Suspicious email from $source.address$ to $destination{}.address$
    risk_objects:
        - field: destination{}.address
          type: user
          score: 20
    threat_objects:
        - field: source.address
          type: email_address
tags:
    analytic_story:
        - Dev Sec Ops
    asset_type: GSuite
    mitre_attack_id:
        - T1566.001
    product:
        - Splunk Enterprise
        - Splunk Enterprise Security
        - Splunk Cloud
    security_domain: endpoint
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/gsuite_susp_url/gsuite_susp_url.log
          source: http:gsuite
          sourcetype: gsuite:gmail:bigquery