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Description

The following analytic detects accounts experiencing a high number of Single Sign-On (SSO) logon errors. It leverages data from the o365_management_activity dataset, focusing on failed user login attempts with SSO errors. This activity is significant as it may indicate brute-force attempts or the hijacking/reuse of SSO tokens. If confirmed malicious, attackers could potentially gain unauthorized access to user accounts, leading to data breaches, privilege escalation, or further lateral movement within the organization.

  • Type: Anomaly

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

  • Last Updated: 2024-05-17
  • Author: Rod Soto, Splunk
  • ID: 8158ccc4-6038-11eb-ae93-0242ac130002

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1556 Modify Authentication Process Credential Access, Defense Evasion, Persistence
Kill Chain Phase
  • Exploitation
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`o365_management_activity` Workload=AzureActiveDirectory LogonError=*Sso* Operation=UserLoginFailed 
| stats count min(_time) as firstTime max(_time) as lastTime values(user) as user by  src_ip signature user_agent authentication_service action
| where count >= 5 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `o365_excessive_sso_logon_errors_filter`

Macros

The SPL above uses the following Macros:

:information_source: o365_excessive_sso_logon_errors_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
  • user
  • src_ip
  • Workload
  • LogonError
  • ActorIpAddress
  • UserAgent
  • UserId
  • authentication_service
  • authentication_method
  • Operation

How To Implement

You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

Known False Positives

Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
64.0 80 80 Excessive number of SSO logon errors from $src_ip$ using UserAgent $user_agent$.

: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.

Reference

Test Dataset

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 | version: 4