Detection rules › Splunk

HTTP PUA User Agent

Status
production
Severity
low
Group by
Web.src, Web.url_length, c-uri, c-useragent, cs-host, cs-method, dest, http_user_agent, src, tool, url
Author
Raven Tait, Splunk
Source
github.com/splunk/security_content

This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of unwanted applications. This activity can signify possible compromised hosts on the network.

Known false positives

  • Noise and false positive can be seen if these programs are allowed to be used within corporate network. In this case, a filter is needed.

MITRE ATT&CK coverage

Rule body

name: HTTP PUA User Agent
id: 21af5447-734f-4549-956b-7a255cb2b032
version: 5
creation_date: '2026-01-06'
modification_date: '2026-05-13'
author: Raven Tait, Splunk
status: production
type: Anomaly
description: This Splunk query analyzes web logs to identify and categorize user agents, detecting various types of unwanted applications. 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 pua_user_agents pua_user_agent AS http_user_agent OUTPUT tool
    | where isnotnull(tool)
    | stats count min(firstTime) as first_seen max(lastTime) as last_seen
      BY tool url http_user_agent
         src dest
    | `security_content_ctime(first_seen)`
    | `security_content_ctime(last_seen)`
    | `http_pua_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: Noise and false positive can be seen if these programs are allowed to be used within corporate network. In this case, a filter is needed.
references:
    - https://github.com/mthcht/awesome-lists/blob/main/Lists/suspicious_http_user_agents_list.csv
intermediate_findings:
    entities:
        - field: src
          type: system
          score: 20
          message: A known user agent ($http_user_agent$) associated with unusual programs was performing a request from $src$.
threat_objects:
    - field: http_user_agent
      type: http_user_agent
analytic_story:
    - Local Privilege Escalation With KrbRelayUp
    - BlackSuit Ransomware
    - Cactus Ransomware
    - Suspicious User Agents
asset_type: Network
mitre_attack_id:
    - T1071.001
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: network
security_domain: network

Stages and Predicates

Stage 1: tstats

| 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

Stage 2: search

| `drop_dm_object_name("Web")`

Stage 3: lookup

| lookup pua_user_agents pua_user_agent AS http_user_agent OUTPUT tool
Lookup table
pua_user_agents
Key field
pua_user_agent as http_user_agent
Output columns
['tool', 'tool']

Stage 4: where

| where isnotnull(tool)

Stage 5: stats

| stats count min(firstTime) as first_seen max(lastTime) as last_seen
  BY tool url http_user_agent
     src dest

Stage 6: search

| `security_content_ctime(first_seen)`

Stage 7: search

| `security_content_ctime(last_seen)`

Stage 8: search

| `http_pua_user_agent_filter`

Indicators

These rows show field, operator, and value matches.