Potentially Relayed NTLM Authentication - Microsoft Sentinel

Group by: Computer, IpAddress, ServiceName, TargetUserName
Author: Cyb3rMonk
Source: github.com/Cyb3r-Monk/Threat-Hunting-and-Detection

The below query detects Kerberos logons of computer accounts where there isn't any ticket request in the last 12h (10h is the default ticket expiration) coming from the same IpAddress with the same TargetUserName. The query can be enriched further if needed.

Event coverage

Provider	Event	Title
Security-Auditing	Event ID 4624	An account was successfully logged on.
Security-Auditing	Event ID 4769	A Kerberos service ticket was requested.

Rule body kusto

// Author       : Cyb3rMonk(https://twitter.com/Cyb3rMonk, https://mergene.medium.com)
//
// Link to original post:
// https://posts.bluraven.io/detecting-kerberos-relaying-e6be66fa647c
//
// Description: This query detects Kerberos logons of computer accounts where there isn't any ticket request in the last 12h (10h is the default ticket expiration) coming from the same IpAddress with the same TargetUserName. The query can be enriched further if needed. 
//
// Query parameters:
//
let Ticket_Requests = materialize ( 
SecurityEvent
| where TimeGenerated > ago(12h)
| where EventID == 4769
| where EventData has '<Data Name="Status">0x0</Data>'
| where EventData !has'<Data Name="IpAddress">::1</Data>'
| parse EventData with * 'TargetUserName">' TargetUserName '</Data' * 'TargetDomainName">' TargetDomainName '</Data' * 'ServiceName">' ServiceName '<' * 'IpAddress">::ffff:' IpAddress '<' * 'Status">' Status '<' *
| where TargetUserName !has ServiceName
| where TargetUserName contains "$"
| where ServiceName has "$"
| project TimeGenerated, TargetUserName=tolower(TargetUserName), TargetDomainName, ServiceName=tolower(replace_string(ServiceName, '$', '')), IpAddress, Status
)
;
let Suspicious_Logons = 
    Ticket_Requests
    | join kind=rightanti (
        SecurityEvent
        | where TimeGenerated > ago(1h)
        | where EventID == 4624
        | where AuthenticationPackageName == "Kerberos"
        | where IpAddress !in ('-', '::1', '127.0.0.1')
        | where IpAddress !startswith "169.254."
        | where Account endswith_cs "$"
        | project TimeGenerated, Computer = tolower(replace_regex(Computer, @'(\w+)\..*', @'\1')), Account, TargetUserName=tolower(TargetUserName), IpAddress
        | where TargetUserName !has Computer
        ) on IpAddress, $left.ServiceName==$right.Computer
        ;
Suspicious_Logons
| join kind=leftouter  (
    Ticket_Requests
    | extend TargetUserName = replace_regex(TargetUserName, @'(\w+\$)@.*', @'\1')
    ) on IpAddress, TargetUserName
| summarize FirstSeen=min(TimeGenerated), LastSeen=max(TimeGenerated), count(), dcount(ServiceName) by TargetUserName, IpAddress
// Filter results
// we don't expect a successful ticket request coming from the rogue(attacker) device befor the relaying attack.
// If there is at least one ticket request coming from the suspicious IP with the same TargetUserName, assume it's a legitimate activity.
| where isempty(dcount_ServiceName)

Stages and Predicates

Stage 0: `let`

let Ticket_Requests = materialize(<inlined as stages below>);
let Suspicious_Logons = Ticket_Requests <inlined as stages below>;

Stage 1: `source`

let Ticket_Requests

Stage 2: `source`

let Suspicious_Logons

Stage 3: `source`

SecurityEvent

Stage 4: `where` time_window=43200s

| where TimeGenerated > ago(12h)

Stage 5: `where`

| where EventID == 4769

Stage 6: `where`

| where EventData has '<Data Name="Status">0x0</Data>'

Stage 7: `where`

| where EventData !has'<Data Name="IpAddress">::1</Data>'

Stage 8: `parse`

| parse EventData with * 'TargetUserName">' TargetUserName '</Data' * 'TargetDomainName">' TargetDomainName '</Data' * 'ServiceName">' ServiceName '<' * 'IpAddress">::ffff:' IpAddress '<' * 'Status">' Status '<' *

Stage 9: `where`

| where TargetUserName !has ServiceName

Stage 10: `where`

| where TargetUserName contains "$"

Stage 11: `where`

| where ServiceName has "$"

Stage 12: `project`

| project TimeGenerated, TargetUserName=tolower(TargetUserName), TargetDomainName, ServiceName=tolower(replace_string(ServiceName, '$', '')), IpAddress, Status

Stage 13: `join`

| join kind=rightanti (
        SecurityEvent
        | where TimeGenerated > ago(1h)
        | where EventID == 4624
        | where AuthenticationPackageName == "Kerberos"
        | where IpAddress !in ('-', '::1', '127.0.0.1')
        | where IpAddress !startswith "169.254."
        | where Account endswith_cs "$"
        | project TimeGenerated, Computer = tolower(replace_regex(Computer, @'(\w+)\..*', @'\1')), Account, TargetUserName=tolower(TargetUserName), IpAddress
        | where TargetUserName !has Computer
        ) on IpAddress, $left.ServiceName==$right.Computer

Stage 14: `join`

| join kind=leftouter  (
    Ticket_Requests
    | extend TargetUserName = replace_regex(TargetUserName, @'(\w+\$)@.*', @'\1')
    ) on IpAddress, TargetUserName

Stage 15: `summarize`

| summarize FirstSeen=min(TimeGenerated), LastSeen=max(TimeGenerated), count(), dcount(ServiceName) by TargetUserName, IpAddress

Stage 16: `where`

| where isempty(dcount_ServiceName)

Exclusions

Top-level NOT(...) conjuncts: predicates this rule actively suppresses.

Stage	Field	Kind	Excluded values
7	`EventData`	match	`<Data Name="IpAddress">::1</Data>`
9	`TargetUserName`	match	`ServiceName`
13	`IpAddress`	in	`-`, `127.0.0.1`, `::1`
13	`IpAddress`	starts_with	`169.254.`
13	`TargetUserName`	match	`Computer`
14	`EventData`	match	`<Data Name="IpAddress">::1</Data>`
14	`TargetUserName`	match	`ServiceName`

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
`Account`	ends_with	`$` transforms: `cased`
`AuthenticationPackageName`	eq	`Kerberos` transforms: `cased` corpus 6 (sigma 2, elastic 2, splunk 1, kusto 1)
`EventData`	match	`<Data Name="Status">0x0</Data>`
`EventID`	eq	`4624` transforms: `cased` corpus 26 (splunk 13, kusto 9, chronicle 4) `4769` transforms: `cased` corpus 10 (splunk 6, kusto 4)
`ServiceName`	match	`$`
`TargetUserName`	contains	`$`
`dcount_ServiceName`	is_null	(no value, null check)

Output fields

Fields the rule emits when it matches. Chronicle authors list these in the outcome block; they appear on the detection and $risk_score drives alerting. Sentinel / Defender XDR rules build them up through project / summarize / extend stages. Sentinel maps these into alert fields via entityMappings and customDetails; Defender XDR custom detections surface them as alert fields directly.

Field	Source
`FirstSeen`	`summarize`
`IpAddress`	`summarize`
`LastSeen`	`summarize`
`TargetUserName`	`summarize`

`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:`)
`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)

Potentially Relayed NTLM Authentication - Microsoft Sentinel

Event coverage

Rule body kusto

Stages and Predicates

Stage 0: `let`

Stage 1: `source`

Stage 2: `source`

Stage 3: `source`

Stage 4: `where` time_window=43200s

Stage 5: `where`

Stage 6: `where`

Stage 7: `where`

Stage 8: `parse`

Stage 9: `where`

Stage 10: `where`

Stage 11: `where`

Stage 12: `project`

Stage 13: `join`

Stage 14: `join`

Stage 15: `summarize`

Stage 16: `where`

Exclusions

Indicators

Output fields

Keyboard shortcuts

Search operators

Potentially Relayed NTLM Authentication - Microsoft Sentinel

Event coverage

Rule body kusto

Stages and Predicates

Stage 0: let

Stage 1: source

Stage 2: source

Stage 3: source

Stage 4: where time_window=43200s

Stage 5: where

Stage 6: where

Stage 7: where

Stage 8: parse

Stage 9: where

Stage 10: where

Stage 11: where

Stage 12: project

Stage 13: join

Stage 14: join

Stage 15: summarize

Stage 16: where

Exclusions

Indicators

Output fields

Stage 0: `let`

Stage 1: `source`

Stage 2: `source`

Stage 3: `source`

Stage 4: `where` time_window=43200s

Stage 5: `where`

Stage 6: `where`

Stage 7: `where`

Stage 8: `parse`

Stage 9: `where`

Stage 10: `where`

Stage 11: `where`

Stage 12: `project`

Stage 13: `join`

Stage 14: `join`

Stage 15: `summarize`

Stage 16: `where`