:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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Description

The following analytic identifies multiple failed app requests in an attempt to identify the reuse a stolen web session cookie. The logic of the analytic is as follows: \ * Retrieves policy evaluation and SSO details in events that contain the Application requested \

  • Formats target fields so we can aggregate specifically on Applications (AppInstances) \
  • Groups by User, Session and IP \
  • Creates a ratio of successful SSO events to total MFA challenges related to Application Sign On Policies \

  • Alerts when more than half of app sign on events are unsuccessful, and challenges were unsatisfied for more than three apps.

  • Type: Hunting

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2023-03-17
  • Author: John Murphy, Okta, Michael Haag, Splunk
  • ID: 1c21fed1-7000-4a2e-9105-5aaafa437247

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1550.004 Web Session Cookie Defense Evasion, Lateral Movement
T1538 Cloud Service Dashboard Discovery
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`okta` target{}.type=AppInstance (eventType=policy.evaluate_sign_on outcome.result=CHALLENGE) OR (eventType=user.authentication.sso outcome.result=SUCCESS) 
| eval targets=mvzip('target{}.type', 'target{}.displayName', ": ") 
| eval targets=mvfilter(targets LIKE "AppInstance%") 
| 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 
| search total_challenges > 0 
| 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 
| fillnull 
| 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" 
| fields - count, targets 
| search ratio < 0.5 total_challenges > 2 
| `okta_multiple_failed_requests_to_access_applications_filter`

Macros

The SPL above uses the following Macros:

:information_source: okta_multiple_failed_requests_to_access_applications_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • authenticationContext.externalSessionId
  • targets
  • actor.alternateId
  • client.ipAddress
  • eventType

How To Implement

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

RBA

Risk Score Impact Confidence Message
56.0 80 70 Multiple Failed Requests to Access Applications via Okta for $actor.alternateId$.

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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 | version: 1