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Okta Mismatch Between Source and Response for Verify Push Request

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.

MITRE ATT&CK

Detection Query

`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
  | 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%", 1, 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 authenticationContext.externalSessionId
  | 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
  | rename actor.alternateId as user
  | `okta_mismatch_between_source_and_response_for_verify_push_request_filter`

Author

John Murphy and Jordan Ruocco, Okta, Michael Haag, Bhavin Patel, Splunk

Created

2026-03-10

Data Sources

Okta

Tags

Okta Account TakeoverOkta MFA ExhaustionScattered Lapsus$ Hunters
Raw Content
name: Okta Mismatch Between Source and Response for Verify Push Request
id: 8085b79b-9b85-4e67-ad63-351c9e9a5e9a
version: 9
date: '2026-03-10'
author: John Murphy and Jordan Ruocco, Okta, Michael Haag, Bhavin Patel, Splunk
type: TTP
status: production
data_source:
    - Okta
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.
search: |-
    `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
      | 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%", 1, 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 authenticationContext.externalSessionId
      | 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
      | rename actor.alternateId as user
      | `okta_mismatch_between_source_and_response_for_verify_push_request_filter`
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.
drilldown_searches:
    - name: View the detection results for - "$user$"
      search: '%original_detection_search% | search  user = "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$user$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") starthoursago=168  | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
references:
    - https://attack.mitre.org/techniques/T1621
    - https://splunkbase.splunk.com/app/6553
rba:
    message: A mismatch between source and response for verifying a push request has occurred for $user$
    risk_objects:
        - field: user
          type: user
          score: 50
    threat_objects: []
tags:
    analytic_story:
        - Okta Account Takeover
        - Okta MFA Exhaustion
        - Scattered Lapsus$ Hunters
    asset_type: Okta Tenant
    mitre_attack_id:
        - T1621
    product:
        - Splunk Enterprise
        - Splunk Enterprise Security
        - Splunk Cloud
    security_domain: access
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1621/okta_mismatch/okta_mismatch.log
          source: Okta
          sourcetype: OktaIM2:log