analyzing-active-directory-acl-abuse

analyzing-active-directory-acl-abuse is a skill for Claude Code from plurigrid/asi. It costs 36 tokens per session (925 once invoked), scanned A, a copy of analyzing-active-directory-acl-abuse, MIT.

A security analysis procedure for finding dangerous permission settings in Microsoft Active Directory. Active Directory is Microsoft's system for managing users, computers, and permissions across an organization.

In plain words
What is it for?
It is for connecting to a domain controller, reading access-control records, and finding permissions such as full control, permission editing, or ownership changes.
Why use it?
It identifies cases where ordinary users may have excessive control over sensitive groups, domain controllers, or policy objects.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the asi plugin — 56 skills shipped together

Good fit It is for connecting to a domain controller, reading access-control records, and finding permissions such as full control, permission editing, or ownership changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/plurigrid/asi/analyzing-active-directory-acl-abuse
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add plurigrid/asi --skill analyzing-active-directory-acl-abuse
Clone the repo
git clone --depth 1 https://github.com/plurigrid/asi

Made for: Claude Code.

Or install asi, the plugin that ships this one along with the rest of its 56 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-active-directory-acl-abuse

README.md
[![agentmods](https://agentmods.dev/badge/skills/plurigrid/asi/analyzing-active-directory-acl-abuse/github.svg)](https://agentmods.dev/skills/plurigrid/asi/analyzing-active-directory-acl-abuse)
Your own site
<a href="https://agentmods.dev/skills/plurigrid/asi/analyzing-active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/analyzing-active-directory-acl-abuse/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for analyzing-active-directory-acl-abuse

Your own site · 80×15
<a href="https://agentmods.dev/skills/plurigrid/asi/analyzing-active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/analyzing-active-directory-acl-abuse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 925 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 86% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00036 $0.00925
Opus 5 $0.00018 $0.00463
Sonnet 5 $0.00007 $0.00185
Haiku 4.5 $0.00004 $0.00093

Measured 6d ago against content hash c98d7f31e0b0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

analyzing-active-directory-acl-abuse scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

86% identical to analyzing-active-directory-acl-abuse — 21 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/asi/skills/analyzing-active-directory-acl-abuse/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

  3. Parse security descriptors: Convert the binary nTSecurityDescriptor into its SDDL string representation. Parse each ACE in the DACL to extract the trustee SID, access mask, and ACE type (allow/deny).

Read the full file on GitHub · 76 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 76 lines · 36 tokens per session scan A c98d7f31e0b0

Subscribe to this mod's changes

analyzing-active-directory-acl-abuse is a skill published in the GitHub repository plurigrid/asi (62 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 925 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to analyzing-active-directory-acl-abuse, differing in 21 lines, and is treated as a copy.

Related

Other skills, from other repositories

analyzing-active-directory-acl-abuse

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

mukul975/Anthropic-Cybersecurity-Skills · 36 tokens

analyzing-active-directory-acl-abuse

A security procedure for finding dangerous permission errors in Active Directory, Microsoft's system for managing users, computers, and groups in a Windows organisation. It checks access-control entries for rights such as full control or permission changes.

killvxk/cybersecurity-skills-zh · 42 tokens

analyzing-active-directory-acl-abuse

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

26zl/cybersec-toolkit · 36 tokens

analyzing-active-directory-acl-abuse

Use when detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths. Use when detecting dangerous acl misconfigurations in active directory using ldap3 to.

oyi77/1ai-skills · 55 tokens

analyzing-active-directory-acl-abuse

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

pinkpixel-dev/skills-collection-1 · 36 tokens

analyzing-active-directory-acl-abuse

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

marysatasselshaped667/skills-collection-1 · 36 tokens