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The following analytic detects potential distributed password spraying attacks in an Azure AD environment. It identifies a spike in failed authentication attempts across various user-and-IP combinations from multiple source IPs and countries, using different user agents. This detection leverages Azure AD SignInLogs, focusing on error code 50126 for failed authentications. This activity is significant as it indicates an adversary's attempt to bypass security controls by distributing login attempts. If confirmed malicious, this could lead to unauthorized access, data breaches, privilege escalation, and lateral movement within the organization's infrastructure.

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2024-05-14
  • Author: Mauricio Velazco, Splunk
  • ID: 116e11a9-63ea-41eb-a66a-6a13bdc7d2c7




ID Technique Tactic
T1586 Compromise Accounts Resource Development
T1586.003 Cloud Accounts Resource Development
T1110 Brute Force Credential Access
T1110.003 Password Spraying Credential Access
T1110.004 Credential Stuffing Credential Access
Kill Chain Phase
  • Weaponization
  • Exploitation
  • DE.AE
  • CIS 10
 `azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false 
| rename properties.* as * 
| bucket span=5m _time 
| eval uniqueIPUserCombo = src_ip . "-" . user 
| stats count min(_time) as firstTime max(_time) as lastTime dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, dc(user_agent) as uniqueUserAgents, dc(location.countryOrRegion) as uniqueCountries values(user) as user, values(src_ip) as ips, values(user_agent) as user_agents, values(location.countryOrRegion) as countries 
| where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20 AND uniqueUserAgents = 1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `azure_ad_multi_source_failed_authentications_spike_filter`


The SPL above uses the following Macros:

:information_source: azure_ad_multi-source_failed_authentications_spike_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • category
  • properties.authenticationDetails{}.succeeded
  • properties.location.countryOrRegion
  • user_agent
  • src_ip
  • user

How To Implement

You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase ( 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 SignInLogs log category. The thresholds set within the analytic (such as unique IPs, unique users, etc.) are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment.

Known False Positives

This detection may yield false positives in scenarios where legitimate bulk sign-in activities occur, such as during company-wide system updates or when users are accessing resources from varying locations in a short time frame, such as in the case of VPNs or cloud services that rotate IP addresses. Filter as needed.

Associated Analytic Story


Risk Score Impact Confidence Message
42.0 70 60 An anomalous multi source authentication spike ocurred at $_time$

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.


Test Dataset

Replay any dataset to Splunk Enterprise by using our tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

source | version: 3