skills/detecting-dcsync-attack-in-active-directory/SKILL.md
Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.
npx skillsauth add mukul975/anthropic-cybersecurity-skills detecting-dcsync-attack-in-active-directoryInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
| Concept | Description | |---------|-------------| | T1003.006 | OS Credential Dumping: DCSync | | DCSync | Mimicking domain controller replication to extract credentials | | DsGetNCChanges | RPC function used to request AD replication data | | DS-Replication-Get-Changes | AD permission required (GUID: 1131f6aa-...) | | DS-Replication-Get-Changes-All | Permission including confidential attributes (GUID: 1131f6ad-...) | | MS-DRSR | Microsoft Directory Replication Service Remote Protocol | | KRBTGT Hash | Key target of DCSync enabling Golden Ticket attacks | | Event ID 4662 | Directory service object access audit event |
| Tool | Purpose | |------|---------| | Mimikatz (lsadump::dcsync) | Primary DCSync attack tool | | Impacket secretsdump.py | Python-based DCSync implementation | | DSInternals | PowerShell module for AD replication | | BloodHound | Map accounts with replication rights | | Splunk / Elastic | SIEM correlation of 4662 events | | Microsoft Defender for Identity | Native DCSync detection | | CrowdStrike Falcon | EDR-based DCSync detection |
index=wineventlog EventCode=4662
| where Properties IN ("*1131f6aa-9c07-11d1-f79f-00c04fc2dcd2*",
"*1131f6ad-9c07-11d1-f79f-00c04fc2dcd2*",
"*89e95b76-444d-4c62-991a-0facbeda640c*")
| where NOT match(SubjectUserName, ".*\\$$")
| where NOT SubjectUserName IN ("known_svc_account1", "known_svc_account2")
| stats count values(Properties) as ReplicationRights by SubjectUserName SubjectDomainName Computer
| where count > 0
| table SubjectUserName SubjectDomainName Computer count ReplicationRights
SecurityEvent
| where EventID == 4662
| where Properties has "1131f6ad-9c07-11d1-f79f-00c04fc2dcd2"
or Properties has "1131f6aa-9c07-11d1-f79f-00c04fc2dcd2"
| where SubjectUserName !endswith "$"
| where SubjectUserName !in ("AzureADConnect", "MSOL_*")
| project TimeGenerated, SubjectUserName, SubjectDomainName, Computer, Properties
| sort by TimeGenerated desc
title: DCSync Activity Detected - Non-DC Replication Request
status: stable
logsource:
product: windows
service: security
detection:
selection:
EventID: 4662
Properties|contains:
- '1131f6aa-9c07-11d1-f79f-00c04fc2dcd2'
- '1131f6ad-9c07-11d1-f79f-00c04fc2dcd2'
filter_dc:
SubjectUserName|endswith: '$'
condition: selection and not filter_dc
level: critical
tags:
- attack.credential_access
- attack.t1003.006
lsadump::dcsync /user:krbtgt to extract KRBTGT hash for Golden Ticket creation.secretsdump.py domain/user:password@dc-ip extracting all domain hashes.Get-ADReplAccount cmdlet to replicate specific account credentials.Hunt ID: TH-DCSYNC-[DATE]-[SEQ]
Alert Severity: Critical
Source Account: [Account requesting replication]
Source Machine: [Hostname/IP of requestor]
Target DC: [Domain controller receiving request]
Replication Rights: [GUIDs accessed]
Timestamp: [Event time]
Legitimate DC: [Yes/No]
Known Service Account: [Yes/No]
Risk Assessment: [Critical - non-DC replication detected]
data-ai
Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.
testing
Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.
tools
Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.
development
Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.