Detection rules › Splunk
Azure AD Unusual Number of Failed Authentications From Ip
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
| Tactic | Techniques |
|---|---|
| Resource Development | T1586.003 Compromise Accounts: Cloud Accounts |
| Credential Access | T1110.003 Brute Force: Password Spraying, T1110.004 Brute Force: Credential Stuffing |
Rules detecting the same action
Other rules on this platform that filter on the same API call or operation.
- Authentication Attempt from New Country (Kusto)
- Azure AD Authentication Failed During MFA Challenge (Splunk)
- Azure AD Device Code Authentication (Splunk)
- Azure AD High Number Of Failed Authentications For User (Splunk)
- Azure AD High Number Of Failed Authentications From Ip (Splunk)
- Azure AD Multi-Source Failed Authentications Spike (Splunk)
- Azure AD Multiple AppIDs and UserAgents Authentication Spike (Splunk)
- Azure AD Multiple Denied MFA Requests For User (Splunk)
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 =unique_accounts > 10 AND unique_accounts >= upperBound10Stage 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.
| Field | Kind | Values |
|---|---|---|
category | eq |
|
isOutlier | eq |
|
properties.authenticationDetails{}.succeeded | eq |
|
properties.status.errorCode | eq |
|
sourcetype | eq |
|