exploiting-active-directory-with-bloodhound

Exploiting Active Directory with BloodHound

Legal Notice: This 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.

Overview

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 find manually. SharpHound is the official data collector that gathers AD objects, relationships, ACLs, sessions, and group memberships.

When to Use

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

Prerequisites

• Familiarity with red teaming concepts and tools
• Access to 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

Workflow

Phase 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

Phase 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. Find AS-REP Roastable accounts
5. Analyze ACL-based attack paths (GenericAll, GenericWrite, WriteDACL, ForceChangePassword)
6. Review GPO abuse opportunities

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

Validation 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