O365 Multi-Source Failed Authentications Spike
Description
The following analytic identifies a spike in failed authentication attempts within an Office 365 environment, indicative of a potential distributed password spraying attack. It leverages UserLoginFailed events from O365 Management Activity logs, focusing on ErrorNumber 50126. This detection is significant as it highlights attempts to bypass security controls using multiple IP addresses and user agents. If confirmed malicious, this activity could lead to unauthorized access, data breaches, privilege escalation, and lateral movement within the organization. Early detection is crucial to prevent account takeovers and mitigate subsequent threats.
- Type: Hunting
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2024-05-31
- Author: Mauricio Velazco, Splunk
- ID: ea4e2c41-dbfb-4f5f-a7b6-9ac1b7f104aa
Annotations
ATT&CK
Kill Chain Phase
- Weaponization
- Exploitation
NIST
- DE.AE
CIS20
- CIS 10
CVE
Search
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`o365_management_activity` Workload=AzureActiveDirectory Operation=UserLoginFailed ErrorNumber=50126
| bucket span=5m _time
| eval uniqueIPUserCombo = src_ip . "-" . user
| stats dc(uniqueIPUserCombo) as uniqueIpUserCombinations, dc(user) as uniqueUsers, dc(src_ip) as uniqueIPs, values(user) as user, values(src_ip) as ips, values(user_agent) as user_agents by _time
| where uniqueIpUserCombinations > 20 AND uniqueUsers > 20 AND uniqueIPs > 20
| `o365_multi_source_failed_authentications_spike_filter`
Macros
The SPL above uses the following Macros:
o365_multi-source_failed_authentications_spike_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
- Workload
- Operation
- ErrorNumber
- user
- src_ip
- user_agent
How To Implement
You must install the Splunk Microsoft Office 365 Add-on and ingest Office 365 management activity events. The thresholds set within the analytic (such as unique IPs, unique users, etc.) are initial guidelines and should be customized based on the organization's user behavior and risk profile. Security teams are encouraged to adjust these thresholds to optimize the balance between detecting genuine threats and minimizing false positives, ensuring the detection is tailored to their specific environment.
Known False Positives
This detection may yield false positives in scenarios where legitimate bulk sign-in activities occur, such as during company-wide system updates or when users are accessing resources from varying locations in a short time frame, such as in the case of VPNs or cloud services that rotate IP addresses. Filter as needed.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
42.0 | 70 | 60 | An anomalous multi source authentication spike ocurred at $_time$ |
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/T1110/003/
- https://docs.microsoft.com/en-us/security/compass/incident-response-playbook-password-spray
- https://www.cisa.gov/uscert/ncas/alerts/aa21-008a
- https://docs.microsoft.com/azure/active-directory/reports-monitoring/reference-sign-ins-error-codes
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