HTTP Malware User Agent

name: HTTP Malware User Agent
id: 8c4866e4-f488-4253-8537-7dc4f954c292
version: 3
date: '2026-03-10'
author: Raven Tait, Splunk
status: production
type: TTP
description: This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of malware. This activity can signify possible compromised hosts on the network.
data_source:
    - Suricata
search: |-
    | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Web
      WHERE Web.http_user_agent != null
      BY Web.http_user_agent Web.http_method, Web.url,
         Web.url_length Web.src, Web.dest
    | `drop_dm_object_name("Web")`
    | lookup malware_user_agents malware_user_agent AS http_user_agent OUTPUT malware
    | where isnotnull(malware)
    | stats count min(firstTime) as first_seen max(lastTime) as last_seen
      BY malware url http_user_agent
         src dest
    | `security_content_ctime(first_seen)`
    | `security_content_ctime(last_seen)`
    | `http_malware_user_agent_filter`
how_to_implement: To successfully implement this search, you need to be ingesting web or proxy logs, or ensure it is being filled by a proxy like device, into the Web Datamodel. For additional filtering, allow list private IP space or restrict by known good.
known_false_positives: Filtering may be required in some instances depending on legacy system usage, filter as needed.
references:
    - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv
drilldown_searches:
    - name: View the detection results for - "$src$"
      search: '%original_detection_search% | search  src = "$src$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$src$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$") 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: A known malware user agent $http_user_agent$ was performing a request from $src$.
    risk_objects:
        - field: src
          type: system
          score: 50
    threat_objects:
        - field: http_user_agent
          type: http_user_agent
tags:
    analytic_story:
        - Lokibot
        - Lumma Stealer
        - Meduza Stealer
        - Crypto Stealer
        - RedLine Stealer
        - Suspicious User Agents
    asset_type: Network
    mitre_attack_id:
        - T1071.001
    product:
        - Splunk Enterprise
        - Splunk Enterprise Security
        - Splunk Cloud
    security_domain: network
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1071.001/http_user_agents/suricata_malware.log
          sourcetype: suricata
          source: not_applicable