| ID | Technique | Tactic |
|---|---|---|
| T1621 | Multi-Factor Authentication Request Generation | Credential Access |
Detection: Okta Mismatch Between Source and Response for Verify Push Request
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
1`okta` eventType IN (system.push.send_factor_verify_push) OR (eventType IN (user.authentication.auth_via_mfa) debugContext.debugData.factor="OKTA_VERIFY_PUSH")
2| eval groupby="authenticationContext.externalSessionId"
3| eval group_push_time=_time
4| bin span=2s group_push_time
5| fillnull value=NULL
6| 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
7| iplocation client.ipAddress
8| fields - lat, lon, group_push_time
9| 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
10| eval ratio = round(total_successes / total_pushes, 2)
11| 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
12|rename actor.alternateId as user
13| `okta_mismatch_between_source_and_response_for_verify_push_request_filter`
Data Source
| Name | Platform | Sourcetype | Source |
|---|---|---|---|
| Okta | N/A | 'OktaIM2:log' |
'Okta' |
Macros Used
| Name | Value |
|---|---|
| okta | eventtype=okta_log OR sourcetype = "OktaIM2:log" |
| okta_mismatch_between_source_and_response_for_verify_push_request_filter | search * |
okta_mismatch_between_source_and_response_for_verify_push_request_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
MITRE ATT&CK
Kill Chain Phases
NIST
CIS
Threat Actors
KillChainPhase.EXPLOITAITON
NistCategory.DE_CM
Cis18Value.CIS_10
APT29
LAPSUS$
Scattered Spider
Default Configuration
This detection is configured by default in Splunk Enterprise Security to run with the following settings:
| Setting | Value |
|---|---|
| Disabled | true |
| Cron Schedule | 0 * * * * |
| Earliest Time | -70m@m |
| Latest Time | -10m@m |
| Schedule Window | auto |
| Creates Notable | Yes |
| Rule Title | %name% |
| Rule Description | %description% |
| Notable Event Fields | user, dest |
| Creates Risk Event | True |
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.
Implementation
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
Risk Based Analytics (RBA)
| Risk Message | Risk Score | Impact | Confidence |
|---|---|---|---|
| A mismatch between source and response for verifying a push request has occurred for $user$ | 64 | 80 | 80 |
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
References
Detection Testing
| Test Type | Status | Dataset | Source | Sourcetype |
|---|---|---|---|---|
| Validation | ✅ Passing | N/A | N/A | N/A |
| Unit | ✅ Passing | Dataset | Okta |
OktaIM2:log |
| Integration | ✅ Passing | Dataset | Okta |
OktaIM2:log |
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: GitHub | Version: 4