Detection rules › Splunk

Detect Password Spray Attack Behavior From Source

Status
production
Severity
medium
Group by
app, authentication_method, dest, event_action, signature, signature_id, src, user
Author
Steven Dick
Source
github.com/splunk/security_content

The following analytic identifies one source failing to authenticate with 10 or more unique users. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises and works well against any number of data sources ingested into the CIM datamodel. Environments can be very different depending on the organization. Test and customize this detections thresholds if needed.

MITRE ATT&CK coverage

TacticTechniques
Credential AccessT1110.003 Brute Force: Password Spraying

Event coverage

Rule body splunk

name: Detect Password Spray Attack Behavior From Source
id: b6391b15-e913-4c2c-8949-9eecc06efacc
version: 12
creation_date: '2023-11-11'
modification_date: '2026-05-13'
author: Steven Dick
status: production
type: TTP
description: The following analytic identifies one source failing to authenticate with 10 or more unique users. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises and works well against any number of data sources ingested into the CIM datamodel. Environments can be very different depending on the organization. Test and customize this detections thresholds if needed.
data_source:
    - Windows Event Log Security 4624
    - Windows Event Log Security 4625
search: |-
    | tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count FROM datamodel=Authentication.Authentication
      BY Authentication.action Authentication.app Authentication.authentication_method
         Authentication.dest Authentication.signature Authentication.signature_id
         Authentication.src Authentication.user
    | `drop_dm_object_name("Authentication")`
    | eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), src=upper(src), success=if(action="success",count,0),success_user=if(action="success",user,null),failure=if(action="failure",count,0), failed_user=if(action="failure",user,null)
    | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_user) as user values(failed_user) as failed_user dc(success_user) as success_dc dc(failed_user) as failed_dc dc(user) as user_dc ,sum(failure) as failure,sum(success) as success
      BY src
    | fields - _time
    | where user_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | `detect_password_spray_attack_behavior_from_source_filter`
how_to_implement: This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs
known_false_positives: Domain controllers, authentication chokepoints, and vulnerability scanners.
references:
    - https://attack.mitre.org/techniques/T1110/003/
    - https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/
    - https://github.com/MarkoH17/Spray365
drilldown_searches:
    - name: View the detection results for - "$src$" and "$user$"
      search: '%original_detection_search% | search  src = "$src$" user = "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$src$" and "$user$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$src$", "$user$") | 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"
finding:
    title: The source [$src$] attempted to access $user_dc$ distinct users a total of $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected.
    entity:
        field: user
        type: user
        score: 50
intermediate_findings:
    entities:
        - field: src
          type: system
          score: 50
          message: The source [$src$] attempted to access $user_dc$ distinct users a total of $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected.
analytic_story:
    - Compromised User Account
asset_type: Account
mitre_attack_id:
    - T1110.003
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: endpoint
security_domain: access
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/generic_password_spray/password_spray_attack.log
          source: XmlWinEventLog:Security
          sourcetype: XmlWinEventLog
      test_type: unit

Stages and Predicates

Stage 1: tstats

| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count FROM datamodel=Authentication.Authentication
  BY Authentication.action Authentication.app Authentication.authentication_method
     Authentication.dest Authentication.signature Authentication.signature_id
     Authentication.src Authentication.user

Stage 2: search

| `drop_dm_object_name("Authentication")`

Stage 3: eval

| eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), src=upper(src), success=if(action="success",count,0),success_user=if(action="success",user,null),failure=if(action="failure",count,0), failed_user=if(action="failure",user,null)
failed_user =
ifaction = "failure"user
elsenull
failure =
ifaction = "failure"count
else0
success =
ifaction = "success"count
else0
success_user =
ifaction = "success"user
elsenull
user =
ifmatch(upper(user), "[a-zA-Z0-9]{3}")upper(user)
elsenull

Stage 4: stats

| stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_user) as user values(failed_user) as failed_user dc(success_user) as success_dc dc(failed_user) as failed_dc dc(user) as user_dc ,sum(failure) as failure,sum(success) as success
  BY src

Stage 5: fields

| fields - _time

Stage 6: where

| where user_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc

Stage 7: search

| `security_content_ctime(firstTime)`

Stage 8: search

| `security_content_ctime(lastTime)`

Stage 9: search

| `detect_password_spray_attack_behavior_from_source_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
user_dcge
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