Tools Hub/
Active Directory Warfare
6 SPECIALIST TOOLS
ELITE TIER
ADCS ESC1-ESC14 & DCSYNC
T1003OS Credential DumpingBloodHound CE AD Attack GraphElite

1. Mental Model & Architectural Core

CORE OPERATIONAL PURPOSE

BloodHound CE (Community Edition) uses graph theory to reveal attack paths in Active Directory that would take humans weeks to manually discover. SharpHound (the collector) gathers AD data, and BloodHound visualizes it as a directed graph where edges represent exploitable relationships GenericAll, WriteDACL, DCSync, GPO links and automatically finds shortest paths to Domain Admin.

MENTAL MODEL ANALOGY

Active Directory is a city with thousands of streets and buildings (users, groups, computers, GPOs). BloodHound is Google Maps for attackers you tell it "I am here (low-priv user) and I want to reach here (Domain Admin)" and it finds you the 3-hop path you could never discover by reading LDAP dumps.

When to Deploy
  • After initial foothold in AD: map attack paths to Domain Admin from your current user
  • Purple team: identify high-risk ACL relationships before an attacker does
  • ADCS enumeration: identify ESC1-ESC14 vulnerable certificate templates
  • Find Kerberoastable accounts with paths to privileged groups
Real-World Scenarios
  • Run "Shortest Paths to Domain Admins" discovers GenericAll → GPO → DC chains in seconds
  • Find users with DCSync rights (DS-Replication-Get-Changes) via custom Cypher queries
  • Map ADCS ESC1 vulnerable templates to users who can enroll for instant privilege escalation
HOW IT WORKS PROTOCOL & MEMORY INTERNALS

SharpHound (C#) or bloodhound-python queries LDAP and SAMR to collect AD objects: users, groups, computers, GPOs, OUs, trusts, ACLs, and session data. This JSON data is ingested into a Neo4j graph database. BloodHound's query engine uses Cypher (graph query language) to find paths via relationship edges. Pre-built queries surface the most dangerous misconfigurations automatically.

2. Syntax, Flags & Live Telemetry

Flags & Options Reference
5 OPTIONS
Flag / OptionDescription
bloodhound-python -u "user" -p "pass" -d corp.local -ns 10.10.10.1 --zipCollect all AD data from Linux using Python collector
SharpHound.exe -c All --zipfilename output.zipCollect all data from Windows with SharpHound (C#)
SharpHound.exe -c ACL,ObjectProps,TrustsCollect only ACLs, object properties, and trust data
Cypher: MATCH p=shortestPath((u:User)-[*1..]->(g:Group {name:"DOMAIN ADMINS@CORP.LOCAL"})) RETURN pFind shortest path from any user to Domain Admins group
Cypher: MATCH (u:User {owned: true}) RETURN uList all nodes you have marked as owned
Full collection from Linux (bloodhound-python)
COMMAND
bloodhound-python -u "svc_backup" -p "Password123!" -d corp.local -ns 10.10.10.1 -c All --zip
Full collection from Linux (bloodhound-python)
LIVE EXEC
[*] Found AD domain: corp.local
[*] Enumerating DC: dc01.corp.local
[+] Users: 1,420 | Groups: 218 | Computers: 347 | GPOs: 42
[+] ACL edges: 14,283 relationships mapped
[!] Shortest Path: svc_backup → GenericAll (Domain Policy GPO) → Tier 0 OUs → SYSTEM
[✓] Zipped output: 20260901_bloodhound.zip (ready for BloodHound ingestion)
FORENSIC ANALYSIS & OPERATOR INSIGHT

After ingestion, immediately run the pre-built query "Find Shortest Paths to Domain Admins" and "Find Principals with DCSync Rights". Mark every compromised account as owned (right-click → Mark as Owned) and re-run paths to see new routes opening from your compromised nodes.

3. Hands-On Practice Labs & Cyber Ranges

Practice environments are curated from PortSwigger, OffSec, HackTheBox, and TryHackMe. Complete these labs to earn credentials and build verified hands-on skills.

4. Detection & Prevention Playbook

MITRE ATT&CK TACTICS
TA0007 DiscoveryTA0006 Credential Access
HOW TO DETECT LOG SOURCES & TELEMETRY
Windows Security Event Log 4662, 4624LDAP query audit logsMicrosoft Defender for Identity alerts
HOW TO PREVENT & MITIGATE
  • Enable Advanced Audit Policies: DS Access, Account Logon, Object Access
  • Monitor for LDAP queries fetching all user/group/computer objects simultaneously
  • Implement Tier 0/1/2 Admin model to reduce lateral movement paths
  • Use Microsoft Defender for Identity (MDI) which detects BloodHound collection patterns