Detection: GCP Successful Single-Factor Authentication

Description

The following analytic identifies a successful single-factor authentication event against Google Cloud Platform (GCP) for an account without Multi-Factor Authentication (MFA) enabled. It uses Google Workspace login event data to detect instances where MFA is not utilized. This activity is significant as it may indicate a misconfiguration, policy violation, or potential account takeover attempt. If confirmed malicious, an attacker could gain unauthorized access to GCP resources, potentially leading to data breaches, service disruptions, or further exploitation within the cloud environment.

1`gws_reports_login` event.name=login_success NOT `gws_login_mfa_methods` 
2| stats count min(_time) as firstTime max(_time) as lastTime by user, src_ip,  login_challenge_method, app, event.name, vendor_account, action 
3|`security_content_ctime(firstTime)` 
4| `security_content_ctime(lastTime)`
5| `gcp_successful_single_factor_authentication_filter`

Data Source

Name Platform Sourcetype Source
Google Workspace login_success N/A 'gws:reports:admin' 'gws:reports:admin'

Macros Used

Name Value
gws_login_mfa_methods event.parameters{}.multiValue{} IN ("backup_code", "google_authenticator", "google_prompt", "idv_any_phone", "idv_preregistered_phone", "internal_two_factor", "knowledge_employee_id", "knowledge_preregistered_email", "login_location", "knowledge_preregistered_phone", "offline_otp", "security_key", "security_key_otp")
gcp_successful_single_factor_authentication_filter search *
gcp_successful_single_factor_authentication_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
T1586 Compromise Accounts Resource Development
T1586.003 Cloud Accounts Resource Development
T1078 Valid Accounts Defense Evasion
T1078.004 Cloud Accounts Initial Access
KillChainPhase.DELIVERY
KillChainPhase.EXPLOITAITON
KillChainPhase.INSTALLATION
KillChainPhase.WEAPONIZATION
NistCategory.DE_CM
Cis18Value.CIS_10
APT29
APT18
APT28
APT29
APT33
APT39
APT41
Akira
Axiom
Carbanak
Chimera
Cinnamon Tempest
Dragonfly
FIN10
FIN4
FIN5
FIN6
FIN7
FIN8
Fox Kitten
GALLIUM
INC Ransom
Indrik Spider
Ke3chang
LAPSUS$
Lazarus Group
Leviathan
OilRig
POLONIUM
PittyTiger
Play
Sandworm Team
Silence
Silent Librarian
Star Blizzard
Suckfly
Threat Group-3390
Volt Typhoon
Wizard Spider
menuPass
APT28
APT29
APT33
APT5
Ke3chang
LAPSUS$

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

You must install the latest version of Splunk Add-on for Google Workspace from Splunkbase (https://splunkbase.splunk.com/app/5556) which allows Splunk administrators to collect Google Workspace event data in Splunk using Google Workspace APIs. Specifically, this analytic leverages the User log events.

Known False Positives

Although not recommended, certain users may be required without multi-factor authentication. Filter as needed

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Successful authentication for user $user$ without MFA 45 50 90
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 gws:reports:login gws:reports:login
Integration ✅ Passing Dataset gws:reports:login gws:reports:login

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: 4