Detection rules › Splunk

Azure AD Unusual Number of Failed Authentications From Ip

Status
production
Severity
low
Group by
_time, aws::recipientAccountId, src, vendor_product
Author
Mauricio Velazco, Gowthamaraj Rajendran, Splunk
Source
github.com/splunk/security_content

The following analytic identifies a single source IP failing to authenticate with multiple valid users, potentially indicating a Password Spraying attack against an Azure Active Directory tenant. It uses Azure SignInLogs data and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual numbers of failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information.

MITRE ATT&CK coverage

Rules detecting the same action

Other rules on this platform that filter on the same API call or operation.

Rule body splunk

name: Azure AD Unusual Number of Failed Authentications From Ip
id: 3d8d3a36-93b8-42d7-8d91-c5f24cec223d
version: 13
creation_date: '2022-07-11'
modification_date: '2026-05-13'
author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk
status: production
type: Anomaly
description: The following analytic identifies a single source IP failing to authenticate with multiple valid users, potentially indicating a Password Spraying attack against an Azure Active Directory tenant. It uses Azure SignInLogs data and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual numbers of failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information.
data_source:
    - Azure Active Directory
search: |-
    `azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false
      | rename properties.* as *
      | bucket span=5m _time
      | stats dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName values(dest) as dest  values(user) as user
        BY _time, src, vendor_account,
           vendor_product
      | eventstats avg(unique_accounts) as ip_avg, stdev(unique_accounts) as ip_std
        BY src
      | eval upperBound=(ip_avg+ip_std*3)
      | eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,0)
      | where isOutlier = 1
      | `azure_ad_unusual_number_of_failed_authentications_from_ip_filter`
how_to_implement: You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.
known_false_positives: A source Ip failing to authenticate with multiple users is not a common for legitimate behavior.
references:
    - https://attack.mitre.org/techniques/T1110/003/
    - https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
    - https://www.cisa.gov/uscert/ncas/alerts/aa21-008a
    - https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes
drilldown_searches:
    - name: View the detection results for - "$userPrincipalName$"
      search: '%original_detection_search% | search  userPrincipalName = "$userPrincipalName$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$userPrincipalName$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$userPrincipalName$") | 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: userPrincipalName
          type: user
          score: 20
          message: Possible Password Spraying attack against Azure AD from source ip $src$
threat_objects:
    - field: src
      type: ip_address
analytic_story:
    - Azure Active Directory Account Takeover
asset_type: Azure Active Directory
mitre_attack_id:
    - T1110.003
    - T1110.004
    - T1586.003
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: 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/password_spraying_azuread/azuread_signin.log
          source: Azure AD
          sourcetype: azure:monitor:aad
      test_type: unit

Stages and Predicates

Stage 1: search

`azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false

Stage 2: rename

| rename properties.* as *

Stage 3: bucket

| bucket span=5m _time

Stage 4: stats

| stats dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName values(dest) as dest  values(user) as user
    BY _time, src, vendor_account,
       vendor_product

Stage 5: eventstats

| eventstats avg(unique_accounts) as ip_avg, stdev(unique_accounts) as ip_std
    BY src

Stage 6: eval

| eval upperBound=(ip_avg+ip_std*3)

Stage 7: eval

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

Stage 8: where

| where isOutlier = 1

Stage 9: search

| `azure_ad_unusual_number_of_failed_authentications_from_ip_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.