Detection: Azure AD Device Code Authentication

Updated Date: 2024-09-30 ID: d68d8732-6f7e-4ee5-a6eb-737f2b990b91 Author: Mauricio Velazco, Gowthamaraj Rajendran, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic identifies Azure Device Code Phishing attacks, which can lead to Azure Account Take-Over (ATO). It leverages Azure AD SignInLogs to detect suspicious authentication requests using the device code authentication protocol. This activity is significant as it indicates potential bypassing of Multi-Factor Authentication (MFA) and Conditional Access Policies (CAPs) through phishing emails. If confirmed malicious, attackers could gain unauthorized access to Azure AD, Exchange mailboxes, and Outlook Web Application (OWA), leading to potential data breaches and unauthorized data access.

Search

1`azure_monitor_aad` category=SignInLogs "properties.authenticationProtocol"=deviceCode 
2| rename properties.* as * 
3| stats count min(_time) as firstTime max(_time) as lastTime by user src_ip, appDisplayName, userAgent 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `azure_ad_device_code_authentication_filter`

Data Source

Name	Platform	Sourcetype	Source
Azure Active Directory	Azure	`'azure:monitor:aad'`	`'Azure AD'`

Macros Used

Name	Value
azure_monitor_aad	`sourcetype=azure:monitor:aad`
azure_ad_device_code_authentication_filter	`search *`

azure_ad_device_code_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
T1528	Steal Application Access Token	Credential Access
T1566	Phishing	Initial Access
T1566.002	Spearphishing Link	Initial Access

KillChainPhase.DELIVERY

KillChainPhase.EXPLOITAITON

NistCategory.DE_CM

Cis18Value.CIS_10

APT28

APT29

Axiom

GOLD SOUTHFIELD

INC Ransom

APT1

APT29

APT3

APT32

APT33

APT39

BlackTech

Cobalt Group

Confucius

EXOTIC LILY

Earth Lusca

Elderwood

Evilnum

FIN4

FIN7

FIN8

Kimsuky

Lazarus Group

LazyScripter

Leviathan

LuminousMoth

Machete

Magic Hound

Mofang

Molerats

MuddyWater

Mustang Panda

Mustard Tempest

OilRig

Patchwork

RedCurl

Sandworm Team

Sidewinder

TA2541

TA505

TA577

Transparent Tribe

Turla

Windshift

Wizard Spider

ZIRCONIUM

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 Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the SignInLogs log category.

Known False Positives

In most organizations, device code authentication will be used to access common Microsoft service but it may be legitimate for others. Filter as needed.

Associated Analytic Story

Azure Active Directory Account Takeover

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
Device code requested for $user$ from $src_ip$	35	70	50

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	`Azure AD`	`azure:monitor:aad`
Integration	✅ Passing	Dataset	`Azure AD`	`azure:monitor:aad`

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