O365 Multiple OS Vendors Authenticating From User

Status: production
Severity: medium
Group by: ClientIP, _time, aws::recipientAccountId, dest, user, vendor_product
Author: Steven Dick
Source: github.com/splunk/security_content

The following analytic identifies when multiple operating systems are used to authenticate to Azure/EntraID/Office 365 by the same user account over a short period of time. This activity could be indicative of attackers enumerating various logon capabilities of Azure/EntraID/Office 365 and attempting to discover weaknesses in the organizational MFA or conditional access configurations. Usage of the tools like "MFASweep" will trigger this detection.

MITRE ATT&CK coverage

Tactic	Techniques
Credential Access	`T1110` Brute Force

Rules detecting the same action

Other rules on this platform that filter on the same API call or operation.

Rule body splunk

name: O365 Multiple OS Vendors Authenticating From User
id: 3451e58a-9457-4985-a600-b616b0cbfda1
version: 7
creation_date: '2025-01-06'
modification_date: '2026-05-13'
author: Steven Dick
status: production
type: TTP
description: The following analytic identifies when multiple operating systems are used to authenticate to Azure/EntraID/Office 365 by the same user account over a short period of time. This activity could be indicative of attackers enumerating various logon capabilities of Azure/EntraID/Office 365 and attempting to discover weaknesses in the organizational MFA or conditional access configurations. Usage of the tools like "MFASweep" will trigger this detection.
data_source:
    - Office 365 Universal Audit Log
search: |-
    `o365_management_activity` Operation IN (UserLoginFailed,UserLoggedIn)
    | eval -time = _time
    | bin _time span=15m
    | fillnull
    | stats values(Operation) as signature, values(ErrorNumber) as signature_id, values(OS) as os_name, dc(OS) as os_count, count, min(-time) as firstTime, max(-time) as lastTime by ClientIP, UserId, _time, dest, vendor_account, vendor_product
    | where os_count >= 4
    | eval src = ClientIP, user = UserId
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | `o365_multiple_os_vendors_authenticating_from_user_filter`
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 OS) 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: IP or users where the usage of multiple Operating systems is expected, filter accordingly.
references:
    - https://attack.mitre.org/techniques/T1110
    - https://www.blackhillsinfosec.com/exploiting-mfa-inconsistencies-on-microsoft-services/
    - https://sra.io/blog/msspray-wait-how-many-endpoints-dont-have-mfa/
    - https://github.com/dafthack/MFASweep/tree/master
drilldown_searches:
    - name: View the detection results for - "$user$"
      search: '%original_detection_search% | search user = "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$user$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") | 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"
    - name: Investigate logons from $user$
      search: '`o365_management_activity` Operation IN (UserLoginFailed,UserLoggedIn) "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
finding:
    title: The user account $user$ authenticated with $os_count$ unique operating system types over a short period from $src$.
    entity:
        field: user
        type: user
        score: 50
threat_objects:
    - field: src
      type: ip_address
analytic_story:
    - Office 365 Account Takeover
asset_type: O365 Tenant
mitre_attack_id:
    - T1110
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: cloud
security_domain: threat
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/azure_mfasweep_events/azure_mfasweep_events.log
          source: o365
          sourcetype: o365:management:activity
      test_type: unit

Stages and Predicates

Stage 1: `search`

`o365_management_activity` Operation IN (UserLoginFailed,UserLoggedIn)

Stage 2: `eval`

| eval -time = _time

Stage 3: `bucket`

| bin _time span=15m

Stage 4: `fillnull`

| fillnull

Stage 5: `stats`

| stats values(Operation) as signature, values(ErrorNumber) as signature_id, values(OS) as os_name, dc(OS) as os_count, count, min(-time) as firstTime, max(-time) as lastTime by ClientIP, UserId, _time, dest, vendor_account, vendor_product

Stage 6: `where`

| where os_count >= 4

Stage 7: `eval`

| eval src = ClientIP, user = UserId

Stage 8: `search`

| `security_content_ctime(firstTime)`

Stage 9: `search`

| `security_content_ctime(lastTime)`

Stage 10: `search`

| `o365_multiple_os_vendors_authenticating_from_user_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
`Operation`	in	`"UserLoggedIn"` `"UserLoginFailed"`
`os_count`	ge	`4`
`sourcetype`	eq	`o365:management:activity`

`j` / `k`	Scroll down / up
`d` / `u`	Half-page down / up
`gg` / `G`	Top / bottom
`h` / `l`	History back / forward
`f`	Follow link (`Shift` = new tab)
`/`	Focus search
`?`	Toggle this help
`↑` / `↓`	Navigate search results
`Enter`	Open highlighted result
`Esc`	Close results / dialog

`type:`	`events` / `rules` / `providers`
`vendor:`	`sigma` / `elastic` / `splunk` / `kusto` / `chronicle` (vendor name alone also works: `sigma:`, `kql:`, `secops:`…)
`tactic:`	TA-id, slug, or name: `credential_access`, `TA0006`
`technique:`	technique or sub-technique ID: `T1003`, `T1003.001` (alias `tech:`)
`severity:`	`critical` / `high` / `medium` / `low` / `informational` (alias `sev:`)
`risk_score`	Numeric comparison on the Elastic risk score (0 to 100): `risk_score>50`, `risk_score<=20`, `risk_score=99` (alias `risk`; Elastic rules only)
`stages:`	Rules with exactly N pipeline stages
`correlation:`	`single_event` / `sequence` / `alternatives` / `alternatives_cross_log` / `all_required` / `correlated`
`with:`	Co-occurrence event-id; stacks (`with:4624 with:4769`) to require all, while a comma list in one occurrence (`with:4624,4769`) is an either-or group. Implies multi-event
`like:`	Structural neighbors of a rule slug (equivalents + subsumption stricter / broader): `like:comsvcs_lsass_memory_dump-splunk-sysmon`
`groupby:`	Entity-grouping substring match against `group_by_keys`: `groupby:user`, `groupby:host`
`uses:`	Rules whose predicate tree touches the field (any kind, any value): `uses:CommandLine`
`excludes:`	Rules with top-level `not()` clauses on the field (FP whitelists): `excludes:ParentImage`
`field:` / `value:`	Predicate search; narrows rule cards to those with a matching leaf and drives the indicator tier. Unquoted = substring, wildcards allowed (`value:mimikatz`)
`indicator:`	Shorthand for `field:F value:V`: `indicator:Image=*\powershell.exe`
`kind:`	Filter by predicate kind. Narrows rule cards to those carrying a matching predicate leaf (`vendor:elastic kind:cidr_match`) and drives the indicator tier: `contains` / `starts_with` / `ends_with` / `regex` / `cidr` / `eq` / `in` … (operator aliases `op:`/`match:`)
`has:` / `no:`	`sample`, `field`, `notes`, `refs`, `trace`, `thirdparty`, `rule`, `pattern`, `timewindow`, `threshold`, `newterms`, `sigma`/`elastic`/`splunk`/`kusto`/`chronicle`
`-op:val`	Exclude matches; works on most operators but not `type:`/`like:`/`has:`/`no:` (use `no:<flag>` to exclude a rule flag): `tactic:execution -vendor:splunk`. Standalone `-kind:`/`-field:`/`-value:` drop every rule carrying a matching predicate leaf (`type:rules -kind:is_null`)
`field:"…"` / `value:"…"`	Quoted value = anchored exact match (also allows spaces): `value:"net user"`
`a,b`	Comma = OR inside one operator (`vendor:sigma,elastic`, `severity:high,critical`); repeating a facet merges the same way. `field:`/`value:` never split (literal commas)
`vendors:` / `stage:`	Singular and plural spellings fold to the canonical operator and value: `tactics:` = `tactic:`, `type:event` = `type:events`, `correlation:sequences` = `correlation:sequence`, `has:thresholds` = `has:threshold`
`"quoted phrase"`	Exact-match a multi-word phrase (free text)

O365 Multiple OS Vendors Authenticating From User

MITRE ATT&CK coverage

Rules detecting the same action

Rule body splunk

Stages and Predicates

Stage 1: `search`

Stage 2: `eval`

Stage 3: `bucket`

Stage 4: `fillnull`

Stage 5: `stats`

Stage 6: `where`

Stage 7: `eval`

Stage 8: `search`

Stage 9: `search`

Stage 10: `search`

Indicators

Keyboard shortcuts

Search operators

O365 Multiple OS Vendors Authenticating From User

MITRE ATT&CK coverage

Rules detecting the same action

Rule body splunk

Stages and Predicates

Stage 1: search

Stage 2: eval

Stage 3: bucket

Stage 4: fillnull

Stage 5: stats

Stage 6: where

Stage 7: eval

Stage 8: search

Stage 9: search

Stage 10: search

Indicators

Stage 1: `search`

Stage 2: `eval`

Stage 3: `bucket`

Stage 4: `fillnull`

Stage 5: `stats`

Stage 6: `where`

Stage 7: `eval`

Stage 8: `search`

Stage 9: `search`

Stage 10: `search`