Detection rules › Splunk
ASL AWS New MFA Method Registered For User
The following analytic identifies the registration of a new Multi-Factor Authentication (MFA) method for an AWS account, as logged through Amazon Security Lake (ASL). It detects this activity by monitoring the CreateVirtualMFADevice API operation within ASL logs. This behavior is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this activity could allow attackers to secure their access, making it harder to detect and remove their presence from the compromised environment.
MITRE ATT&CK coverage
| Tactic | Techniques |
|---|---|
| Persistence | T1556.006 Modify Authentication Process: Multi-Factor Authentication |
| Defense Impairment | T1556.006 Modify Authentication Process: Multi-Factor Authentication |
| Credential Access | T1556.006 Modify Authentication Process: Multi-Factor Authentication |
Rules detecting the same action
Other rules on this platform that filter on the same API call or operation.
Rule body splunk
name: ASL AWS New MFA Method Registered For User
id: 33ae0931-2a03-456b-b1d7-b016c5557fbd
version: 14
creation_date: '2023-01-31'
modification_date: '2026-05-13'
author: Patrick Bareiss, Splunk
status: production
type: TTP
description: The following analytic identifies the registration of a new Multi-Factor Authentication (MFA) method for an AWS account, as logged through Amazon Security Lake (ASL). It detects this activity by monitoring the `CreateVirtualMFADevice` API operation within ASL logs. This behavior is significant because adversaries who gain unauthorized access to an AWS account may register a new MFA method to maintain persistence. If confirmed malicious, this activity could allow attackers to secure their access, making it harder to detect and remove their presence from the compromised environment.
data_source:
- ASL AWS CloudTrail
search: |-
`amazon_security_lake` api.operation=CreateVirtualMFADevice
| fillnull
| stats count min(_time) as firstTime max(_time) as lastTime
BY actor.user.uid api.operation api.service.name
http_request.user_agent src_endpoint.ip actor.user.account.uid
cloud.provider cloud.region
| rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `asl_aws_new_mfa_method_registered_for_user_filter`
how_to_implement: The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
known_false_positives: Newly onboarded users who are registering an MFA method for the first time will also trigger this detection.
references:
- https://aws.amazon.com/blogs/security/you-can-now-assign-multiple-mfa-devices-in-iam/
- https://attack.mitre.org/techniques/T1556/
- https://attack.mitre.org/techniques/T1556/006/
- https://twitter.com/jhencinski/status/1618660062352007174
drilldown_searches:
- name: View the detection results for - "$dest$"
search: '%original_detection_search% | search dest = "$dest$"'
earliest_offset: $info_min_time$
latest_offset: $info_max_time$
- name: View risk events for the last 7 days for - "$dest$"
search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
earliest_offset: 7d
latest_offset: "0"
finding:
title: A new virtual device is added to user $user$
entity:
field: user
type: user
score: 50
threat_objects:
- field: src
type: ip_address
analytic_story:
- AWS Identity and Access Management Account Takeover
asset_type: AWS Account
mitre_attack_id:
- T1556.006
product:
- Splunk Enterprise
- Splunk Enterprise Security
- Splunk Cloud
category: cloud
security_domain: identity
tests:
- name: True Positive Test
attack_data:
- data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556.006/aws_new_mfa_method_registered_for_user/asl_ocsf_cloudtrail.json
sourcetype: aws:asl
source: aws_asl
test_type: unit
Stages and Predicates
Stage 1: search
`amazon_security_lake` api.operation=CreateVirtualMFADevice
Stage 2: fillnull
| fillnull
Stage 3: stats
| stats count min(_time) as firstTime max(_time) as lastTime
BY actor.user.uid api.operation api.service.name
http_request.user_agent src_endpoint.ip actor.user.account.uid
cloud.provider cloud.region
Stage 4: rename
| rename actor.user.uid as user api.operation as action api.service.name as dest http_request.user_agent as user_agent src_endpoint.ip as src actor.user.account.uid as vendor_account cloud.provider as vendor_product cloud.region as vendor_region
Stage 5: search
| `security_content_ctime(firstTime)`
Stage 6: search
| `security_content_ctime(lastTime)`
Stage 7: search
| `asl_aws_new_mfa_method_registered_for_user_filter`
Indicators
Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.
| Field | Kind | Values |
|---|---|---|
api.operation | eq |
|
sourcetype | eq |
|