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Description

The following analytic identifies the provisioning of a new MFA device shortly after a password reset. It detects this activity by correlating Windows Event Log events for password changes (EventID 4723, 4724) with PingID logs indicating device pairing. This behavior is significant as it may indicate a social engineering attack where a threat actor impersonates a valid user to reset credentials and add a new MFA device. If confirmed malicious, this activity could allow an attacker to gain persistent access to the compromised account, bypassing traditional security measures.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Change
  • Last Updated: 2024-05-21
  • Author: Steven Dick
  • ID: 2fcbce12-cffa-4c84-b70c-192604d201d0

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1621 Multi-Factor Authentication Request Generation Credential Access
T1556.006 Multi-Factor Authentication Credential Access, Defense Evasion, Persistence
T1098.005 Device Registration Persistence, Privilege Escalation
Kill Chain Phase
  • Exploitation
  • Installation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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`pingid` "result.message" = "*Device Paired*" 
| rex field=result.message "Device (Unp)?(P)?aired (?<device_extract>.+)" 
| eval src = coalesce('resources{}.ipaddress','resources{}.devicemodel'), user = upper('actors{}.name'), reason = 'result.message' 
| eval object=CASE(ISNOTNULL('resources{}.devicemodel'),'resources{}.devicemodel',true(),device_extract) 
| eval action=CASE(match('result.message',"Device Paired*"),"created",match('result.message', "Device Unpaired*"),"deleted") 
| stats count min(_time) as firstTime, max(_time) as lastTime, values(reason) as reason by src,user,action,object 
| join type=outer user [
| search `wineventlog_security` EventID IN(4723,4724) 
| eval PW_Change_Time = _time, user = upper(user) 
| fields user,src_user,EventID,PW_Change_Time] 
| eval timeDiffRaw = round(lastTime - PW_Change_Time) 
| eval timeDiff = replace(tostring(abs(timeDiffRaw) ,"duration"),"(\d*)\+*(\d+):(\d+):(\d+)","\2 hours \3 minutes") 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(PW_Change_Time)` 
| where timeDiffRaw > 0 AND timeDiffRaw < 3600 
| `pingid_new_mfa_method_after_credential_reset_filter`

Macros

The SPL above uses the following Macros:

:information_source: pingid_new_mfa_method_after_credential_reset_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
  • user
  • src_user
  • src
  • EventID
  • resources{}.ipaddress
  • actors{}.name
  • result.message
  • resources{}.devicemodel

How To Implement

Target environment must ingest Windows Event Log and PingID(PingOne) data sources. Specifically from logs from Active Directory Domain Controllers and JSON logging from a PingID(PingOne) enterprise environment, either via Webhook or Push Subscription.

Known False Positives

False positives may be generated by normal provisioning workflows that generate a password reset followed by a device registration.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
50.0 100 50 An MFA configuration change was detected for [$user$] within [$timeDiff$] of a password reset. The device [$object$] was $action$.

: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: 2