Exploiting Active Directory With Bloodhound

---
name: exploiting-active-directory-with-bloodhound
description: BloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify
  attac
tags:
- bloodhound
- red-team
- exploitation
- fetih
- mitre-attack
- post-exploitation
- cybersecurity
- red-teaming
- active-directory
- siber-güvenlik
- adversary-simulation
triggers:
- active
- adversary emulation
- bloodhound
- directory
- exploit
- exploiting
- kırmızı takım
- log
- malware
- network
- offensive security
- password
category: red-team-operations
source_subdomain: red-teaming
nist_csf:
- ID.RA-01
- GV.OV-02
- DE.AE-07
adapted_for: fetih
---

# Exploiting Active Directory with Bloodhound


> **Legal Notice:** bu skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.

## Genel Bakış

BloodHound is a graph-based Active Directory reconnaissance tool that uses graph theory to reveal hidden and unintended relationships within AD environments. Red teams use BloodHound to identify attack paths from compromised accounts to high-value targets such as Domain Admins, identifying privilege escalation chains that would be nearly impossible to Bul: manually. SharpHound is the official data collector that gathers AD objects, relationships, ACLs, sessions, and group memberships.


## Ne Zaman Kullanılır

- performing yaparken authorized security testing that involves exploiting active directory with bloodhound
- analyzing yaparken malware samples or attack artifacts in a controlled environment
- conducting yaparken red team exercises or penetration testing engagements
- building yaparken Tespit capabilities based on offensive technique understanding

## Ön Gereksinimler

- Familiarity with red teaming concepts and tools
- Erişim: a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities

## Objectives

- Collect Active Directory relationship data using SharpHound or BloodHound.py
- Visualize attack paths from compromised accounts to Domain Admin
- Identify misconfigured ACLs, group memberships, and delegation settings
- Discover shortest attack paths to high-value targets
- Map Kerberos delegation configurations for abuse
- Document all identified privilege escalation chains

## MITRE ATT&CK Mapping

- **T1087.002** - Account Discovery: Domain Account
- **T1069.002** - Permission Groups Discovery: Domain Groups
- **T1482** - Domain Trust Discovery
- **T1615** - Group Policy Discovery
- **T1018** - Remote System Discovery
- **T1033** - System Owner/User Discovery
- **T1016** - System Network Configuration Discovery

## İş Akışı

### Aşama 1: Data Collection with SharpHound
1. Transfer SharpHound collector to compromised host
2. Execute collection with appropriate method (All, DCOnly, Session, LoggedOn)
3. Collect from all reachable domains if multi-domain environment
4. Exfiltrate ZIP data files to analysis workstation
5. Import data into BloodHound CE or Legacy

### Aşama 2: Attack Path Analysis
1. Mark owned principals (compromised accounts)
2. Query shortest path to Domain Admins
3. Identify Kerberoastable accounts with admin privileges
4. Bul: AS-REP Roastable accounts
5. Analyze ACL-based attack paths (GenericAll, GenericWrite, WriteDACL, ForceChangePassword)
6. Review GPO abuse opportunities

### Aşama 3: Exploitation Planning
1. Prioritize attack paths by complexity and stealth
2. Identify required tools for each step in the chain
3. Plan OPSEC considerations for each technique
4. Execute identified attack chain
5. Document evidence at each step

## Tools and Resources

| Tool | Purpose | Platform |
|------|---------|----------|
| BloodHound CE | Graph visualization and analysis | Web-based |
| SharpHound | AD data collection (.NET) | Windows |
| BloodHound.py | AD data collection (Python) | Linux/Windows |
| Cypher queries | Custom graph queries | Neo4j/BloodHound |
| PlumHound | Automated BloodHound reporting | Python |
| Max (BloodHound) | BloodHound automation | Python |

## Key BloodHound Queries

| Query | Purpose |
|-------|---------|
| Shortest Path to Domain Admins | Bul: fastest route to DA |
| Bul: Kerberoastable Users with Path to DA | SPN accounts leading to DA |
| Bul: AS-REP Roastable Users | Accounts without pre-auth |
| Shortest Path from Owned Principals | Paths from compromised accounts |
| Bul: Computers with Unsupported OS | Legacy systems for exploitation |
| Bul: Users with DCSync Rights | Accounts that can replicate AD |
| Bul: GPOs that Modify Local Group Membership | GPO-based privilege escalation |

## Doğrulama Criteria

- [ ] SharpHound data collected from all domains
- [ ] Attack paths identified from owned accounts to DA
- [ ] ACL-based attack paths documented
- [ ] Kerberoastable and AS-REP roastable accounts identified
- [ ] Exploitation plan created with prioritized paths
- [ ] Evidence screenshots captured for report

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