Exploiting Active Directory With Bloodhound

---
name: exploiting-active-directory-with-bloodhound
description: Use when 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. Use when working with
  exploiting active directory with bloodhound.
domain: cybersecurity
subdomain: red-teaming
tags:
- red-team
- adversary-simulation
- mitre-attack
- exploitation
- post-exploitation
- active-directory
- bloodhound
version: '1.0'
author: oyi77
license: Apache-2.0
d3fend_techniques:
- Restore Access
- Password Authentication
- Biometric Authentication
- Strong Password Policy
- Restore User Account Access
nist_csf:
- ID.RA-01
- GV.OV-02
- DE.AE-07
category: cybersecurity
---

# 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
**Trigger phrases:**
- "exploiting active directory with bloodhound"
- "BloodHound is a graph-based Active Directory reconnaissance tool that uses graph"


- 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

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Scope and authorize** — confirm written authorization and define target boundaries
2. **Reconnaissance** — enumerate targets, services, and potential attack surfaces
3. **Exploitation** — attempt exploitation of identified vulnerabilities within scope
4. **Post-exploitation** — document access level, lateral movement, and data exposure
5. **Report and remediate** — compile findings with reproduction steps and fix recommendations
### 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

## When NOT to Use

- You don't have explicit authorization to exploit
- Task is about detecting exploits, not performing them (use detecting-* skills)
- You need to analyze exploit artifacts (use analyzing-* skills)
- Task is about building exploit tools (use building-* skills)
- Target is production without authorization
- Task requires responsible disclosure (follow disclosure process)


## Red Flags

- Performing actions without explicit written authorization from the asset owner
- Testing against production systems without a defined scope and rules of engagement
- Exceeding the authorized scope of the engagement
- Leaving persistent access mechanisms without explicit approval
- Causing denial-of-service on production systems during testing

## Verification

- All steps executed successfully against a test environment before production use
- Output documented with screenshots or logs demonstrating expected behavior
- All exploited vulnerabilities documented with reproduction steps
- Scope boundaries confirmed — only authorized targets were tested
- Remediation recommendations included for every finding

## 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

## Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |