Detection rules › Splunk

Kerberos User Enumeration

Status
production
Severity
low
Group by
_time, src_ip
Author
Mauricio Velazco, Splunk
Source
github.com/splunk/security_content

The following analytic detects an unusual number of Kerberos Ticket Granting Ticket (TGT) requests for non-existing users from a single source endpoint. It leverages Event ID 4768 and identifies anomalies using the 3-sigma statistical rule. This behavior is significant as it may indicate an adversary performing a user enumeration attack against Active Directory. If confirmed malicious, the attacker could validate a list of usernames, potentially leading to further attacks such as brute force or credential stuffing, compromising the security of the environment.

MITRE ATT&CK coverage

Event coverage

Rule body splunk

name: Kerberos User Enumeration
id: d82d4af4-a0bd-11ec-9445-3e22fbd008af
version: 13
creation_date: '2022-03-11'
modification_date: '2026-05-13'
author: Mauricio Velazco, Splunk
status: production
type: Anomaly
description: The following analytic detects an unusual number of Kerberos Ticket Granting Ticket (TGT) requests for non-existing users from a single source endpoint. It leverages Event ID 4768 and identifies anomalies using the 3-sigma statistical rule. This behavior is significant as it may indicate an adversary performing a user enumeration attack against Active Directory. If confirmed malicious, the attacker could validate a list of usernames, potentially leading to further attacks such as brute force or credential stuffing, compromising the security of the environment.
data_source:
    - Windows Event Log Security 4768
search: |-
    `wineventlog_security` EventCode=4768 Status=0x6 TargetUserName!="*$"
      | bucket span=2m _time
      | stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest
        BY _time, src_ip
      | eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std
        BY src_ip
      | eval upperBound=(comp_avg+comp_std*3)
      | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0)
      | search isOutlier=1
      | `kerberos_user_enumeration_filter`
how_to_implement: To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.
known_false_positives: Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.
references:
    - https://github.com/ropnop/kerbrute
    - https://attack.mitre.org/techniques/T1589/002/
    - https://redsiege.com/tools-techniques/2020/04/user-enumeration-part-3-windows/
drilldown_searches:
    - name: View the detection results for - "$src_ip$"
      search: '%original_detection_search% | search  src_ip = "$src_ip$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$src_ip$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src_ip$") | 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"
intermediate_findings:
    entities:
        - field: src_ip
          type: system
          score: 20
          message: Potential Kerberos based user enumeration attack $src_ip$
analytic_story:
    - Active Directory Kerberos Attacks
asset_type: Endpoint
mitre_attack_id:
    - T1589.002
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: endpoint
security_domain: endpoint
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1589.002/kerberos_user_enumeration/windows-xml.log
          source: XmlWinEventLog:Security
          sourcetype: XmlWinEventLog
      test_type: unit

Stages and Predicates

Stage 1: search

`wineventlog_security` EventCode=4768 Status=0x6 TargetUserName!="*$"

Stage 2: bucket

| bucket span=2m _time

Stage 3: stats

| stats dc(TargetUserName) AS unique_accounts values(TargetUserName) as tried_accounts values(dest) as dest
    BY _time, src_ip

Stage 4: eventstats

| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std
    BY src_ip

Stage 5: eval

| eval upperBound=(comp_avg+comp_std*3)

Stage 6: eval

| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0)
isOutlier =
ifunique_accounts > 10 AND unique_accounts >= upperBound1
else0

Stage 7: search

| search isOutlier=1

Stage 8: search

| `kerberos_user_enumeration_filter`

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

FieldKindValues
EventCodeeq
  • 4768 corpus 13 (splunk 11, kusto 2)
Statuseq
  • 0x6 corpus 5 (splunk 3, sigma 2)
TargetUserNamene
  • "*$" corpus 14 (splunk 14)
isOutliereq
  • 1 corpus 28 (splunk 28)