Name:Okta Mismatch Between Source and Response for Verify Push Request id:8085b79b-9b85-4e67-ad63-351c9e9a5e9a version:11 date:None author:John Murphy and Jordan Ruocco, Okta, Michael Haag, Bhavin Patel, Splunk status:production type:TTP 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. Data_source:
-Okta
search:`okta` eventType IN (system.push.send_factor_verify_push) OR (eventType IN (user.authentication.auth_via_mfa) debugContext.debugData.factor="OKTA_VERIFY_PUSH")
| 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
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. References: -https://attack.mitre.org/techniques/T1621 -https://splunkbase.splunk.com/app/6553 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 ("$user$") | 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:'7d' latest_offset:'0' analytic_story:['Okta Account Takeover', 'Okta MFA Exhaustion', 'Scattered Lapsus$ Hunters']
