Detection: Okta Multiple Failed Requests to Access Applications

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2024-10-17 ID: 1c21fed1-7000-4a2e-9105-5aaafa437247 Author: John Murphy, Okta, Michael Haag, Splunk Type: Hunting Product: Splunk Enterprise Security

Description

The following analytic detects multiple failed attempts to access applications in Okta, potentially indicating the reuse of a stolen web session cookie. It leverages Okta logs to evaluate policy and SSO events, aggregating data by user, session, and IP. The detection triggers when more than half of the app sign-on attempts are unsuccessful across multiple applications. This activity is significant as it may indicate an attempt to bypass authentication mechanisms. If confirmed malicious, it could lead to unauthorized access to sensitive applications and data, posing a significant security risk.

Search

 1`okta` target{}.type=AppInstance (eventType=policy.evaluate_sign_on outcome.result=CHALLENGE) OR (eventType=user.authentication.sso outcome.result=SUCCESS) 
 2| eval targets=mvzip('target{}.type', 'target{}.displayName', ": ") 
 3| eval targets=mvfilter(targets LIKE "AppInstance%") 
 4| stats count min(_time) as _time values(outcome.result) as outcome.result dc(eval(if(eventType="policy.evaluate_sign_on",targets,NULL))) as total_challenges sum(eval(if(eventType="user.authentication.sso",1,0))) as total_successes by authenticationContext.externalSessionId targets actor.alternateId client.ipAddress 
 5| search total_challenges > 0 
 6| stats min(_time) as _time values(*) as * sum(total_challenges) as total_challenges sum(total_successes) as total_successes values(eval(if("outcome.result"="SUCCESS",targets,NULL))) as success_apps values(eval(if(":outcome.result"!="SUCCESS",targets,NULL))) as no_success_apps by authenticationContext.externalSessionId actor.alternateId client.ipAddress 
 7| fillnull 
 8| eval ratio=round(total_successes/total_challenges,2), severity="HIGH", mitre_technique_id="T1538", description="actor.alternateId". " from " . "client.ipAddress" . " seen opening " . total_challenges . " chiclets/apps with " . total_successes . " challenges successfully passed" 
 9| fields - count, targets 
10| search ratio < 0.5 total_challenges > 2 
11| `okta_multiple_failed_requests_to_access_applications_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_multiple_failed_requests_to_access_applications_filter	`search *`

okta_multiple_failed_requests_to_access_applications_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
T1550.004	Web Session Cookie	Defense Evasion
T1538	Cloud Service Dashboard	Lateral Movement

KillChainPhase.EXPLOITAITON

NistCategory.DE_AE

Cis18Value.CIS_10

Star Blizzard

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 Risk Event	False

This configuration file applies to all detections of type hunting.

Implementation

This analytic is specific to Okta and requires Okta:im2 logs to be ingested.

Known False Positives

False positives may be present based on organization size and configuration of Okta.

Associated Analytic Story

Okta Account Takeover

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
Multiple Failed Requests to Access Applications via Okta for $actor.alternateId$.	56	80	70

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	Not Applicable	N/A	N/A	N/A
Unit	❌ Failing	N/A	`N/A`	`N/A`
Integration	❌ Failing	N/A	`N/A`	`N/A`

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