:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

Description

The following anomaly identifies failed Okta SSO events utilizing the legacy Okta event "unauth app access attempt".

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

  • Last Updated: 2022-09-21
  • Author: Michael Haag, Rico Valdez, Splunk
  • ID: 371a6545-2618-4032-ad84-93386b8698c5

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1078 Valid Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
T1078.001 Default Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
Kill Chain Phase
  • Exploitation
  • Installation
  • Delivery
NIST
  • DE.AE
CIS20
  • CIS 10
CVE
1
2
3
4
5
`okta` eventType=app.generic.unauth_app_access_attempt 
| stats min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by src_user, result ,displayMessage, src_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `okta_failed_sso_attempts_filter` 

Macros

The SPL above uses the following Macros:

:information_source: okta_failed_sso_attempts_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
  • displayMessage
  • app
  • src_user
  • result
  • src_ip

How To Implement

This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

Known False Positives

There may be a faulty config preventing legitmate users from accessing apps they should have access to.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
16.0 40 40 $src_user$ failed SSO authentication to the app.

: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: 3