analyzing-active-directory-acl-abuse

analyzing-active-directory-acl-abuse is a skill for Claude Code from killvxk/cybersecurity-skills-zh. It costs 42 tokens per session (941 once invoked), scanned A, original, Apache-2.0.

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.

In plain words
What is it for?
Use it to connect to a domain controller, inspect directory permissions, translate security descriptors, identify account owners, and flag risky rights held by non-administrators.
Why use it?
Misconfigured permissions can let ordinary accounts take over sensitive groups, computers, or policies; this procedure helps identify those paths.

Skill for Claude Code

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

Part of the cybersecurity-skills-zh plugin — 58 skills shipped together

Good fit Use it to connect to a domain controller, inspect directory permissions, translate security descriptors, identify account owners, and flag risky rights held by non-administrators.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/killvxk/cybersecurity-skills-zh/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 killvxk/cybersecurity-skills-zh --skill analyzing-active-directory-acl-abuse
Clone the repo
git clone --depth 1 https://github.com/killvxk/cybersecurity-skills-zh

Made for: Claude Code.

Or install cybersecurity-skills-zh, the plugin that ships this one along with the rest of its 58 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/killvxk/cybersecurity-skills-zh/analyzing-active-directory-acl-abuse/github.svg)](https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-active-directory-acl-abuse)
Your own site
<a href="https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/killvxk/cybersecurity-skills-zh/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/killvxk/cybersecurity-skills-zh/analyzing-active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/killvxk/cybersecurity-skills-zh/analyzing-active-directory-acl-abuse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 941 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 original No closer match found 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 $0.00042 $0.00941
Opus 5 $0.00021 $0.00470
Sonnet 5 $0.00008 $0.00188
Haiku 4.5 $0.00004 $0.00094

Measured 11d ago against content hash 4f4ca25ecaf2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, 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 11d 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.

skills/analyzing-active-directory-acl-abuse/SKILL.md · 65 lines

What it actually says

概述

活动目录(Active Directory)访问控制列表(ACL)通过包含访问控制条目(ACE)的自主访问控制列表(DACL)来定义 AD 对象的权限。配置错误的 ACE 可能赋予非特权用户对敏感对象(如 Domain Admins 组、域控制器或 GPO)的危险权限,包括 GenericAll(完全控制)、WriteDACL(修改权限)、WriteOwner(获取所有权)和 GenericWrite(修改属性)。

本技能使用 ldap3 Python 库连接到域控制器,查询包含 nTSecurityDescriptor 属性的对象,将二进制安全描述符解析为 SDDL(安全描述符定义语言)格式,并识别向非管理员主体授予危险权限的 ACE。这些配置错误正是 BloodHound 等工具发现的基于 ACL 的攻击路径的基础。

前置条件

  • Python 3.9 或更高版本,并安装 ldap3 库(pip install ldap3
  • 具有 AD 对象读取权限的域用户凭据
  • 域控制器端口 389(LDAP)或 636(LDAPS)的网络连接
  • 了解 Active Directory 安全模型和 SDDL 格式

步骤

  1. 连接域控制器:使用 ldap3 通过 NTLM 或简单身份验证建立 LDAP 连接。在生产环境中使用 LDAPS(端口 636)进行加密连接。

  2. 查询目标对象:搜索目标 OU 或整个域,获取包括用户、组、计算机和 OU 在内的对象。请求 nTSecurityDescriptordistinguishedNameobjectClasssAMAccountName 属性。

  3. 解析安全描述符:将二进制 nTSecurityDescriptor 转换为 SDDL 字符串表示。解析 DACL 中的每个 ACE,提取受托人 SID、访问掩码和 ACE 类型(允许/拒绝)。

  4. 将 SID 解析为主体:通过对域的 LDAP 查询,将安全标识符(SID)映射为可读的账户名。识别内置组的已知 SID。

  5. 检查危险权限:将每个 ACE 的访问掩码与危险权限位掩码进行比较:GenericAll(0x10000000)、WriteDACL(0x00040000)、WriteOwner(0x00080000)、GenericWrite(0x40000000),以及针对特定扩展权限的 WriteProperty。

  6. 过滤非管理员受托人:排除预期的管理员受托人(Domain Admins、Enterprise Admins、SYSTEM、Administrators),并标记非特权用户或组持有危险权限的 ACE。

  7. 映射攻击路径:对于每个发现,记录潜在的攻击链(例如,对用户的 GenericAll 允许重置密码,对组的 WriteDACL 允许将自身添加到组中)。

  8. 生成修复报告:输出包含所有危险 ACE、受影响对象、非管理员受托人及建议修复步骤的 JSON 报告。

预期输出

{
  "domain": "corp.example.com",
  "objects_scanned": 1247,
  "dangerous_aces_found": 8,
  "findings": [
    {
      "severity": "critical",
      "target_object": "CN=Domain Admins,CN=Users,DC=corp,DC=example,DC=com",
      "target_type": "group",
      "trustee": "CORP\\helpdesk-team",
      "permission": "GenericAll",
      "access_mask": "0x10000000",
      "ace_type": "ACCESS_ALLOWED",
      "attack_path": "GenericAll on Domain Admins group allows adding arbitrary members",
      "remediation": "Remove GenericAll ACE for helpdesk-team on Domain Admins"
    }
  ]
}
Files

What ships with it

3 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. 11d ago First seen · 65 lines · 42 tokens per session scan A 4f4ca25ecaf2

Subscribe to this mod's changes

analyzing-active-directory-acl-abuse is a skill published in the GitHub repository killvxk/cybersecurity-skills-zh (44 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 42 tokens to every session and 941 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.