analyzing-active-directory-acl-abuse

analyzing-active-directory-acl-abuse is a skill for Claude Code, Codex from Youngmaidainon/Agent-Level-Up. It costs 36 tokens per session (992 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 manages users, computers, groups, and permissions in many Windows networks; ACLs are lists controlling who can change each object.

In plain words
What is it for?
Use it to query directory permissions, inspect security descriptors, and detect GenericAll, WriteDACL, WriteOwner, or GenericWrite access that could enable abuse.
Why use it?
It can reveal when ordinary users have excessive control over sensitive groups, domain controllers, or policies. Such permissions can create paths to higher privileges.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to query directory permissions, inspect security descriptors, and detect GenericAll, WriteDACL, WriteOwner, or GenericWrite access that could enable abuse.

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Install with agentmods
npx agentmods add skills/youngmaidainon/agent-level-up/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 Youngmaidainon/Agent-Level-Up --skill analyzing-active-directory-acl-abuse
Clone the repo
git clone --depth 1 https://github.com/Youngmaidainon/Agent-Level-Up

Made for: Claude Code, Codex.

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/youngmaidainon/agent-level-up/analyzing-active-directory-acl-abuse/github.svg)](https://agentmods.dev/skills/youngmaidainon/agent-level-up/analyzing-active-directory-acl-abuse)
Your own site
<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/analyzing-active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/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/youngmaidainon/agent-level-up/analyzing-active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/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 992 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 100% 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 $0.00992
Opus 5 $0.00018 $0.00496
Sonnet 5 $0.00007 $0.00198
Haiku 4.5 $0.00004 $0.00099

Measured 10d ago against content hash e72ab21e752e, 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 10d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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

100% identical to analyzing-active-directory-acl-abuse — 0 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.

cyber-security/ctf/analyzing-active-directory-acl-abuse/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 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 · 91 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 10d ago First seen · 91 lines · 36 tokens per session scan A e72ab21e752e

Subscribe to this mod's changes

analyzing-active-directory-acl-abuse is a skill published in the GitHub repository Youngmaidainon/Agent-Level-Up (3 stars, last pushed 15d ago), licensed MIT. It adds 36 tokens to every session and 992 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to analyzing-active-directory-acl-abuse, differing in 0 lines, and is treated as a copy.

Related

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