# Analyzing Active Directory Acl Abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify...

- Repository: https://github.com/mukul975/Anthropic-Cybersecurity-Skills (35398 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-08-31
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-active-directory-acl-abuse -g -a claude-code -y`
Codex: `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-active-directory-acl-abuse -g -a codex -y`
Cursor: `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-active-directory-acl-abuse -g -a cursor -y`
Gemini CLI: `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-active-directory-acl-abuse -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add mukul975/Anthropic-Cybersecurity-Skills`

## Description

Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Analyzing Active Directory ACL Abuse

## Overview

Active Directory Access Control Lists (ACLs) define permissions on AD objects through Discretionary Access Control Lists (DACLs) containing Access Control Entries (ACEs). Misconfigured ACEs can grant non-privileged users dangerous permissions such as GenericAll (full control), WriteDACL (modify permissions), WriteOwner (take ownership), and GenericWrite (modify attributes) on sensitive objects like Domain Admins groups, domain controllers, or GPOs.

This skill uses the ldap3 Python library to connect to a Domain Controller, query objects with their nTSecurityDescriptor attribute, parse the binary security descriptor into SDDL (Security Descriptor Definition Language) format, and identify ACEs that grant dangerous permissions to non-administrative principals. These misconfigurations are the basis for ACL-based attack paths discovered by tools like BloodHound.


## When to Use

- When investigating security incidents that require analyzing active directory acl abuse
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques

## Prerequisites

- Python 3.9 or later with ldap3 library (`pip install ldap3`)
- Domain user credentials with read access to AD objects
- Network connectivity to Domain Controller on port 389 (LDAP) or 636 (LDAPS)
- Understanding of Active Directory security model and SDDL format

## Steps

1. **Connect to Domain Controller**: Establish an LDAP connection using ldap3 with NTLM or simple authentication. Use LDAPS (port 636) for encrypted connections in production.

2. **Query target objects**: Search the target OU or entire domain for objects including users, groups, computers, and OUs. Request the `nTSecurityDescriptor`, `distinguishedName`, `objectClass`, and `sAMAccountName` attributes.

The rest: https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/HEAD/skills/analyzing-active-directory-acl-abuse/SKILL.md