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.
npx skills add 26zl/cybersec-toolkit --skill analyzing-active-directory-acl-abusegit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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.
[](https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-active-directory-acl-abuse)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/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.
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-active-directory-acl-abuse"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-active-directory-acl-abuse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 |
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.
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.
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.
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
-
Connect to Domain Controller: Establish an LDAP connection using ldap3 with NTLM or simple authentication. Use LDAPS (port 636) for encrypted connections in production.
-
Query target objects: Search the target OU or entire domain for objects including users, groups, computers, and OUs. Request the
nTSecurityDescriptor,distinguishedName,objectClass, andsAMAccountNameattributes. -
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).
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.
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.
- 11d ago First seen · 91 lines · 36 tokens per session scan A e72ab21e752e
analyzing-active-directory-acl-abuse is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed today), 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.
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.
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.
analyzing-active-directory-acl-abuse
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths.
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.
ad-exploitation
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ad-recon
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