Detection: Okta MFA Exhaustion Hunt

Description

The following analytic detects patterns of successful and failed Okta MFA push attempts to identify potential MFA exhaustion attacks. It leverages Okta event logs, specifically focusing on push verification events, and uses statistical evaluations to determine suspicious activity. This activity is significant as it may indicate an attacker attempting to bypass MFA by overwhelming the user with push notifications. If confirmed malicious, this could lead to unauthorized access, compromising the security of the affected accounts and potentially the entire environment.

 1`okta` eventType=system.push.send_factor_verify_push OR ((legacyEventType=core.user.factor.attempt_success) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) OR ((legacyEventType=core.user.factor.attempt_fail) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) 
 2| stats count(eval(legacyEventType="core.user.factor.attempt_success"))  as successes count(eval(legacyEventType="core.user.factor.attempt_fail")) as failures count(eval(eventType="system.push.send_factor_verify_push")) as pushes by user,_time 
 3| stats latest(_time) as lasttime earliest(_time) as firsttime sum(successes) as successes sum(failures) as failures sum(pushes) as pushes by user 
 4| eval seconds=lasttime-firsttime 
 5| eval lasttime=strftime(lasttime, "%c") 
 6| search (pushes>1) 
 7| eval totalattempts=successes+failures 
 8| eval finding="Normal authentication pattern" 
 9| eval finding=if(failures==pushes AND pushes>1,"Authentication attempts not successful because multiple pushes denied",finding) 
10| eval finding=if(totalattempts==0,"Multiple pushes sent and ignored",finding) 
11| eval finding=if(successes>0 AND pushes>3,"Probably should investigate. Multiple pushes sent, eventual successful authentication!",finding) 
12| `okta_mfa_exhaustion_hunt_filter`

Data Source

Name Platform Sourcetype Source Supported App
Okta N/A 'OktaIM2:log' 'Okta' N/A

Macros Used

Name Value
okta eventtype=okta_log OR sourcetype = "OktaIM2:log"
okta_mfa_exhaustion_hunt_filter search *
okta_mfa_exhaustion_hunt_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
ID Technique Tactic
T1110 Brute Force Credential Access
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_10
APT28
APT38
APT39
DarkVishnya
Dragonfly
FIN5
Fox Kitten
HEXANE
OilRig
Turla

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 Risk Event False
This configuration file applies to all detections of type hunting.

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. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning is complete.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
$user$ account has rejected multiple Okta pushes. 18 30 60
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: 3