O365 Concurrent Sessions From Different Ips
Description
The following analytic identies scenarios where the same user session is accessed from multiple IP addresses. This situation typically arises in an adversary-in-the-middle (AiTM) phishing attack, where attackers compromise user sessions. The detection method involves analyzing Azure Active Directory logs for 'UserLoggedIn' operations. It focuses on identifying sessions where the number of associated IP addresses exceeds one for the same SessionId. This pattern suggests potential unauthorized concurrent access, which is atypical under normal usage scenarios. If a true positive is identified, it implies that an adversary has gained unauthorized access to a user's Office 365 account. The ramifications of this can be significant, including data theft, account takeover, and launching of internal phishing campaigns.
- Type: TTP
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2023-12-04
- Author: Mauricio Velazco, Splunk
- ID: 58e034de-1f87-4812-9dc3-a4f68c7db930
Annotations
Kill Chain Phase
- Exploitation
NIST
- DE.CM
CIS20
- CIS 10
CVE
Search
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`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoggedIn
| stats min(_time) as firstTime max(_time) as lastTime values(src_ip) as ips values(user_agent) as user_agents by Operation, user, SessionId
| where mvcount(ips) > 1
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `o365_concurrent_sessions_from_different_ips_filter`
Macros
The SPL above uses the following Macros:
o365_concurrent_sessions_from_different_ips_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
- Operation
- Workload
- src_ip
- user
- user_agent
How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity
Known False Positives
Unknown
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
42.0 | 70 | 60 | User $user$ has logged in with the same session id from more than one unique IP address |
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
- https://attack.mitre.org/techniques/T1185/
- https://breakdev.org/evilginx-2-next-generation-of-phishing-2fa-tokens/
- https://github.com/kgretzky/evilginx2
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