: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.

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Description

The following analytic identifies discrepancies between the source and response events for Okta Verify Push requests, indicating potential suspicious behavior. It leverages Okta System Log events, specifically system.push.send_factor_verify_push and user.authentication.auth_via_mfa with the factor "OKTA_VERIFY_PUSH." The detection groups events by SessionID, calculates the ratio of successful sign-ins to push requests, and checks for session roaming and new device/IP usage. This activity is significant as it may indicate push spam or unauthorized access attempts. If confirmed malicious, attackers could bypass MFA, leading to unauthorized access to sensitive systems.

  • Type: TTP

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

  • Last Updated: 2024-05-19
  • Author: John Murphy and Jordan Ruocco, Okta, Michael Haag, Splunk
  • ID: 8085b79b-9b85-4e67-ad63-351c9e9a5e9a

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1621 Multi-Factor Authentication Request Generation Credential Access
Kill Chain Phase
  • Exploitation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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`okta` eventType IN (system.push.send_factor_verify_push) OR (eventType IN (user.authentication.auth_via_mfa) debugContext.debugData.factor="OKTA_VERIFY_PUSH") 
| eval groupby="authenticationContext.externalSessionId" 
| eval group_push_time=_time 
| bin span=2s group_push_time 
| fillnull value=NULL 
| stats min(_time) as _time by authenticationContext.externalSessionId eventType debugContext.debugData.factor outcome.result actor.alternateId client.device client.ipAddress client.userAgent.rawUserAgent debugContext.debugData.behaviors group_push_time groupby 
| iplocation client.ipAddress 
| fields - lat, lon, group_push_time 
| stats min(_time) as _time dc(client.ipAddress) as dc_ip sum(eval(if(eventType="system.push.send_factor_verify_push" AND "outcome.result"="SUCCESS",1,0))) as total_pushes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND "outcome.result"="SUCCESS",1,0))) as total_successes sum(eval(if(eventType="user.authentication.auth_via_mfa" AND "outcome.result"="FAILURE",1,0))) as total_rejected sum(eval(if(eventType="system.push.send_factor_verify_push" AND "debugContext.debugData.behaviors" LIKE "%New Device=POSITIVE%",1,0))) as suspect_device_from_source sum(eval(if(eventType="system.push.send_factor_verify_push" AND "debugContext.debugData.behaviors" LIKE "%New IP=POSITIVE%",0,0))) as suspect_ip_from_source values(eval(if(eventType="system.push.send_factor_verify_push","client.ipAddress",""))) as src values(eval(if(eventType="user.authentication.auth_via_mfa","client.ipAddress",""))) as dest values(*) as * by groupby 
| eval ratio = round(total_successes/total_pushes,2) 
| search ((ratio < 0.5 AND total_pushes > 1) OR (total_rejected > 0)) AND dc_ip > 1 AND suspect_device_from_source > 0 AND suspect_ip_from_source > 0 
| `okta_mismatch_between_source_and_response_for_verify_push_request_filter`

Macros

The SPL above uses the following Macros:

:information_source: okta_mismatch_between_source_and_response_for_verify_push_request_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
  • authenticationContext.externalSessionId
  • eventType
  • debugContext.debugData.factor
  • outcome.result
  • actor.alternateId
  • client.device
  • client.ipAddress
  • client.userAgent.rawUserAgent
  • debugContext.debugData.behaviors
  • group_push_time

How To Implement

The analytic leverages Okta OktaIm2 logs to be ingested using the Splunk Add-on for Okta Identity Cloud (https://splunkbase.splunk.com/app/6553).

Known False Positives

False positives may be present based on organization size and configuration of Okta. Monitor, tune and filter as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
64.0 80 80 A mismatch between source and response for verifying a push request has occurred for $actor.alternateId$

: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: 2