Detection rules › Splunk
Windows Unusual NTLM Authentication Users By Destination
The following analytic detects when a device is the target of numerous NTLM authentications using a null domain. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device from a non-domain device. This activity may also generate a large number of EventID 4776 events in tandem, however these events will not indicate the attacker or target device.
MITRE ATT&CK coverage
| Tactic | Techniques |
|---|---|
| Credential Access | T1110.003 Brute Force: Password Spraying |
Rule body splunk
name: Windows Unusual NTLM Authentication Users By Destination
id: 1120a204-8444-428b-8657-6ea4e1f3e840
version: 10
creation_date: '2024-03-16'
modification_date: '2026-05-13'
author: Steven Dick
status: production
type: Anomaly
description: The following analytic detects when a device is the target of numerous NTLM authentications using a null domain. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device from a non-domain device. This activity may also generate a large number of EventID 4776 events in tandem, however these events will not indicate the attacker or target device.
data_source:
- NTLM Operational 8004
- NTLM Operational 8005
- NTLM Operational 8006
search: |
`ntlm_audit`
EventCode = 8004
SChannelName=*
WorkstationName=*
```CIM alignment, remove leading \\ from some auth attempts ```
| eval src = replace(WorkstationName,"\\\\","")
| eval dest = SChannelName, user = UserName
``` Remove NTLM auths to self, improves accuracy for certain applications ```
| where SChannelName!=src
| stats count min(_time) as firstTime
max(_time) as lastTime
dc(eval(upper(user))) as unique_count by dest
| eventstats avg(unique_count) as unique_avg
stdev(unique_count) as unique_std
```adjust formula for sensitivity```
| eval upperBound_unique=(1+unique_avg+unique_std*3)
| eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0)
| where isOutlier==1
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `windows_unusual_ntlm_authentication_users_by_destination_filter`
how_to_implement: The following analytic detects when an unusual number of NTLM authentications is attempted against the same destination. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device using an NTLM based process/attack. This same activity may also generate a large number of EventID 4776 events as well.
known_false_positives: Vulnerability scanners, print servers, and applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise.
references:
- https://attack.mitre.org/techniques/T1110/003/
- https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191
- https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827
- https://www.varonis.com/blog/investigate-ntlm-brute-force
- https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f
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"
intermediate_findings:
entities:
- field: dest
type: system
score: 20
message: The device [$dest$] was the target of $count$ NTLM authentications using $unique_count$ unique user accounts.
analytic_story:
- Active Directory Password Spraying
asset_type: Endpoint
mitre_attack_id:
- T1110.003
product:
- Splunk Enterprise
- Splunk Enterprise Security
- Splunk Cloud
category: endpoint
security_domain: endpoint
tests:
- name: True Positive Test
attack_data:
- data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/ntlm_bruteforce/ntlm_bruteforce.log
source: XmlWinEventLog:Microsoft-Windows-NTLM/Operational
sourcetype: XmlWinEventLog
test_type: unit
Stages and Predicates
Stage 1: search
`ntlm_audit`
EventCode = 8004
SChannelName=*
WorkstationName=*
Stage 2: eval
| eval src = replace(WorkstationName,"\\\\","")
Stage 3: eval
| eval dest = SChannelName, user = UserName
Stage 4: where
| where SChannelName!=src
Stage 5: stats
| stats count min(_time) as firstTime
max(_time) as lastTime
dc(eval(upper(user))) as unique_count by dest
Stage 6: eventstats
| eventstats avg(unique_count) as unique_avg
stdev(unique_count) as unique_std
Stage 7: eval
| eval upperBound_unique=(1+unique_avg+unique_std*3)
Stage 8: eval
| eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0)
isOutlier =unique_count > upperBound_unique10Stage 9: where
| where isOutlier==1
Stage 10: search
| `security_content_ctime(firstTime)`
Stage 11: search
| `security_content_ctime(lastTime)`
Stage 12: search
| `windows_unusual_ntlm_authentication_users_by_destination_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 |
|---|---|---|
EventCode | eq |
|
SChannelName | eq |
|
WorkstationName | eq |
|
sourcetype | eq |
|