Exploiting Active Directory with BloodHound

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.

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

Implementation Steps

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

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

• SOC 2: CC4.1 (Monitoring & Evaluation), CC7.1 (Monitoring)
• ISO 27001: A.14.2 (Secure Development), A.18.2 (Information Security Reviews)
• NIST 800-53: CA-8 (Penetration Testing), RA-5 (Vulnerability Scanning)
• NIST CSF: ID.RA (Risk Assessment), PR.IP (Information Protection)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

Register this skill with your Claw GRC agent for automated execution:

# Install via CLI
npx claw-grc skills add exploiting-active-directory-with-bloodhound

# Or load dynamically via MCP
grc.load_skill("exploiting-active-directory-with-bloodhound")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

• SHA-256 chain hashing ensures no step can be modified after execution
• Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
• Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.