Detect Password Spray Attempts

name: Detect Password Spray Attempts
id: 086ab581-8877-42b3-9aee-4a7ecb0923af
version: 12
creation_date: '2024-07-01'
modification_date: '2026-05-13'
author: Dean Luxton
status: production
type: TTP
description: This analytic employs the 3-sigma approach to detect an unusual volume of failed authentication attempts from a single source. A password spray attack is a type of brute force attack where an attacker tries a few common passwords across many different accounts to avoid detection and account lockouts. By utilizing the Authentication Data Model, this detection is effective for all CIM-mapped authentication events, providing comprehensive coverage and enhancing security against these attacks.
data_source:
    - Windows Event Log Security 4625
search: "| tstats `security_content_summariesonly` values(Authentication.user) AS unique_user_names dc(Authentication.user) AS unique_accounts values(Authentication.app) as app count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action=\"failure\" NOT Authentication.src IN (\"-\",\"unknown\") by Authentication.action Authentication.app Authentication.authentication_method Authentication.dest \n  Authentication.signature Authentication.signature_id Authentication.src sourcetype _time span=5m  \n| `drop_dm_object_name(\"Authentication\")`\n    ```fill out time buckets for 0-count events during entire search length```\n| appendpipe [| timechart limit=0 span=5m count | table _time] | fillnull value=0 unique_accounts\n  ``` Create aggregation field & apply to all null events```\n| eval counter=src+\"__\"+sourcetype+\"__\"+signature_id  | eventstats values(counter) as fnscounter  | eval counter=coalesce(counter,fnscounter) \n  ``` stats version of mvexpand ```\n| stats values(app) as app values(unique_user_names) as unique_user_names values(total_failures) as total_failures values(src) as src values(signature_id) as signature_id values(sourcetype) as sourcetype count by counter unique_accounts _time\n    ``` remove duplicate time buckets for each unique source```\n| sort - _time unique_accounts | dedup _time counter\n    ```Find the outliers```\n| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by counter | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_accounts > 30 and unique_accounts >= upperBound, 1, 0) | replace \"::ffff:*\" with * in src  | where isOutlier=1  | foreach * \n    [ eval <<FIELD>> = if(<<FIELD>>=\"null\",null(),<<FIELD>>)] \n| table _time, src, action, app, unique_accounts, unique_user_names, total_failures, sourcetype, signature_id, counter | `detect_password_spray_attempts_filter`"
how_to_implement: 'Ensure in-scope authentication data is CIM mapped and the src field is populated with the source device.  Also ensure fill_nullvalue is set within the macro security_content_summariesonly. This search opporates best on a 5 minute schedule, looking back over the past 70 minutes.  Configure 70 minute throttling on the two fields _time and counter. '
known_false_positives: No false positives have been identified at this time.
references:
    - https://attack.mitre.org/techniques/T1110/003/
finding:
    title: Potential Password Spraying attack from $src$ targeting $unique_accounts$ unique accounts.
    entity:
        field: unique_user_names
        type: user
        score: 50
threat_objects:
    - field: src
      type: system
analytic_story:
    - Compromised User Account
    - Active Directory Password Spraying
asset_type: Endpoint
atomic_guid:
    - 90bc2e54-6c84-47a5-9439-0a2a92b4b175
mitre_attack_id:
    - T1110.003
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: application
security_domain: access