Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Adversaries may attempt to gather information on domain trust relationships that may be used to identify lateral movement opportunities in Windows multi-domain/forest environments.
.claude/skills/cyberstrikeus-t1482-domain-trust-discovery/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -8% | 0% |
Adversaries may attempt to gather information on domain trust relationships that may be used to identify lateral movement opportunities in Windows multi-domain/forest environments. Domain trusts provide a mechanism for a domain to allow access to resources based on the authentication procedures of another domain. Domain trusts allow the users of the trusted domain to access resources in the trusting domain. The information discovered may help the adversary conduct SID-History Injection, Pass the Ticket, and Kerberoasting. Domain trusts can be enumerated using the DSEnumerateDomainTrusts() Win32 API call, .NET methods, and LDAP. The Windows utility Nltest is known to be used by adversaries to enumerate domain trusts.
Platforms: Windows
The following tests are from Atomic Red Team and provide actionable ways to test this technique:
Uses the dsquery command to discover domain trusts. Requires the installation of dsquery via Windows RSAT or the Windows Server AD DS role.
Supported Platforms: windows
cmddsquery * -filter "(objectClass=trustedDomain)" -attr *
Uses the nltest command to discover domain trusts. Requires the installation of nltest via Windows RSAT or the Windows Server AD DS role. This technique has been used by the Trickbot malware family.
Supported Platforms: windows
cmdnltest /domain_trusts nltest /trusted_domains
Dependencies:
Use powershell to enumerate AD information. Requires the installation of PowerShell AD admin cmdlets via Windows RSAT or the Windows Server AD DS role.
Supported Platforms: windows
powershellImport-Module "PathToAtomicsFolder\..\ExternalPayloads\PowerView.ps1" Get-NetDomainTrust Get-NetForestTrust Get-ADDomain Get-ADGroupMember Administrators -Recursive ([System.DirectoryServices.ActiveDirectory.Domain]::GetCurrentDomain()).GetAllTrustRelationships()
Dependencies:
Adfind tool can be used for reconnaissance in an Active directory environment. This example has been documented by ransomware actors enumerating Active Directory OUs reference- http://www.joeware.net/freetools/tools/adfind/, https://www.fireeye.com/blog/threat-research/2019/04/pick-six-intercepting-a-fin6-intrusion.html
Supported Platforms: windows
cmd"PathToAtomicsFolder\..\ExternalPayloads\AdFind.exe" -f (objectcategory=organizationalUnit) #{optional_args}
Dependencies:
Adfind tool can be used for reconnaissance in an Active directory environment. This example has been documented by ransomware actors enumerating Active Directory Trusts reference- http://www.joeware.net/freetools/tools/adfind/, https://www.fireeye.com/blog/threat-research/2019/04/pick-six-intercepting-a-fin6-intrusion.html
Supported Platforms: windows
cmd"PathToAtomicsFolder\..\ExternalPayloads\AdFind.exe" #{optional_args} -gcb -sc trustdmp
Dependencies:
If Atomic Red Team tests are not applicable, manually verify the technique by:
Map the trusts within existing domains/forests and keep trust relationships to a minimum.
Employ network segmentation for sensitive domains..
| Finding | Severity | Impact | | ------------------------------------------- | -------- | --------- | | Domain Trust Discovery technique applicable | High | Discovery |
| CWE ID | Title | | ------- | --------------------------------- | | CWE-200 | Exposure of Sensitive Information |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | fail→pass | 6,711 | 9,273 | +38% | 1 | 1 | 0% | 1,311 | 1,829 | +40% | 0 | 0 | — |
case-01 | fail→pass | 10,293 | 11,522 | +12% | 1 | 1 | 0% | 1,711 | 2,691 | +57% | 0 | 0 | — |
case-02 | fail→fail | 15,374 | 17,984 | +17% | 1 | 1 | 0% | 2,658 | 4,296 | +62% | 0 | 0 | — |
case-03 | fail→fail | 21,253 | 22,879 | +8% | 1 | 1 | 0% | 3,272 | 4,668 | +43% | 0 | 0 | — |
case-04 | pass→pass | 8,852 | 5,238 | -41% | 1 | 1 | 0% | 615 | 2,048 | +233% | 0 | 0 | — |
case-06 | pass→pass | 4,825 | 4,281 | -11% | 1 | 1 | 0% | 888 | 2,347 | +164% | 0 | 0 | — |
case-07 | fail→pass | 10,083 | 8,172 | -19% | 1 | 1 | 0% | 1,922 | 2,048 | +7% | 0 | 0 | — |
case-08 | pass→pass | 9,309 | 10,930 | +17% | 1 | 1 | 0% | 1,697 | 1,911 | +13% | 0 | 0 | — |
case-09 | pass→pass | 12,897 | 11,421 | -11% | 1 | 1 | 0% | 1,448 | 2,100 | +45% | 0 | 0 | — |
case-10 | pass→pass | 11,656 | 2,960 | -75% | 1 | 1 | 0% | 1,150 | 2,054 | +79% | 0 | 0 | — |
case-11 | pass→pass | 3,447 | 7,777 | +126% | 1 | 1 | 0% | 595 | 2,043 | +243% | 0 | 0 | — |
case-12 | pass→pass | 12,485 | 8,099 | -35% | 1 | 1 | 0% | 1,296 | 2,085 | +61% | 0 | 0 | — |
case-13 | fail→pass | 20,948 | 8,264 | -61% | 1 | 1 | 0% | 1,434 | 2,067 | +44% | 0 | 0 | — |
case-14 | fail→pass | 17,643 | 4,840 | -73% | 1 | 1 | 0% | 2,399 | 2,217 | -8% | 0 | 0 | — |
case-15 | fail→pass | 21,965 | 2,436 | -89% | 1 | 1 | 0% | 2,517 | 1,945 | -23% | 0 | 0 | — |
case-16 | pass→pass | 16,984 | 2,182 | -87% | 1 | 1 | 0% | 2,218 | 1,956 | -12% | 0 | 0 | — |
case-17 | pass→pass | 17,543 | 1,596 | -91% | 1 | 1 | 0% | 2,242 | 1,764 | -21% | 0 | 0 | — |
case-18 | pass→pass | 16,714 | 7,727 | -54% | 1 | 1 | 0% | 1,599 | 2,012 | +26% | 0 | 0 | — |
case-19 | fail→pass | 17,508 | 9,010 | -49% | 1 | 1 | 0% | 1,663 | 1,871 | +13% | 0 | 0 | — |
case-20 | pass→pass | 6,843 | 12,235 | +79% | 1 | 1 | 0% | 1,134 | 2,891 | +155% | 0 | 0 | — |
case-21 | pass→pass | 12,107 | 9,647 | -20% | 1 | 1 | 0% | 1,194 | 2,249 | +88% | 0 | 0 | — |
case-22 | pass→fail | 17,462 | 29,798 | +71% | 1 | 1 | 0% | 2,732 | 3,582 | +31% | 0 | 0 | — |
case-23 | pass→pass | 22,370 | 20,731 | -7% | 1 | 1 | 0% | 2,816 | 4,169 | +48% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 22 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +26 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.