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 |
Detection: Azure AD Unusual Number of Failed Authentications From Ip
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.
Search
1`azure_monitor_aad` category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false
2| rename properties.* as *
3| bucket span=5m _time
4| stats dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName by _time, ipAddress
5| eventstats avg(unique_accounts) as ip_avg, stdev(unique_accounts) as ip_std by ipAddress
6| eval upperBound=(ip_avg+ip_std*3)
7| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,0)
8| where isOutlier = 1
9| `azure_ad_unusual_number_of_failed_authentications_from_ip_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
Azure Active Directory | Azure | 'azure:monitor:aad' |
'Azure AD' |
N/A |
Macros Used
Name | Value |
---|---|
azure_monitor_aad | sourcetype=azure:monitor:aad |
azure_ad_unusual_number_of_failed_authentications_from_ip_filter | search * |
azure_ad_unusual_number_of_failed_authentications_from_ip_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
Default Configuration
This detection is configured by default in Splunk Enterprise Security to run with the following settings:
Setting | Value |
---|---|
Disabled | true |
Cron Schedule | 0 * * * * |
Earliest Time | -70m@m |
Latest Time | -10m@m |
Schedule Window | auto |
Creates Risk Event | True |
Implementation
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.
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
Possible Password Spraying attack against Azure AD from source ip $ipAddress$ | 54 | 60 | 90 |
References
-
https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
-
https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes
Detection Testing
Test Type | Status | Dataset | Source | Sourcetype |
---|---|---|---|---|
Validation | ✅ Passing | N/A | N/A | N/A |
Unit | ✅ Passing | Dataset | Azure AD |
azure:monitor:aad |
Integration | ✅ Passing | Dataset | Azure AD |
azure:monitor:aad |
Replay any dataset to Splunk Enterprise by using our replay.py
tool or the UI.
Alternatively you can replay a dataset into a Splunk Attack Range
Source: GitHub | Version: 4