Detect Password Spray Attack Behavior On User

name: Detect Password Spray Attack Behavior On User
id: a7539705-7183-4a12-9b6a-b6eef645a6d7
version: 10
date: '2026-03-10'
author: Steven Dick
status: production
type: TTP
description: The following analytic identifies any user failing to authenticate from 10 or more unique sources. 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. Environments can be very different depending on the organization. Test and customize this detections thresholds as 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), success=if(action="success",count,0), src=upper(src), success_src=if(action="success",src,null), failure=if(action="failure",count,0), failed_src=if(action="failure",src,null)
    | stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_src) as src values(failed_src) as failed_src dc(success_src) as success_dc dc(failed_src) as failed_dc dc(src) as src_dc, sum(failure) as failure, sum(success) as success
      BY user
    | fields - _time
    | where src_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | `detect_password_spray_attack_behavior_on_user_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 ("$src$", "$user$") 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 total of $src_dc$ distinct sources attempted to access the account [$user$], $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected.
    risk_objects:
        - field: src
          type: system
          score: 50
        - field: user
          type: user
          score: 50
    threat_objects: []
tags:
    analytic_story:
        - Compromised User Account
        - Crypto Stealer
    asset_type: Account
    mitre_attack_id:
        - T1110.003
    product:
        - Splunk Enterprise
        - Splunk Enterprise Security
        - Splunk Cloud
    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