Analyzing Active Directory Acl AbuseSAFE
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·
Overview
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·
6c59587be632OBSERVED · 2026-10-07What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: analyzing-active-directory-acl-abuse
description: Detect dangerous ACL misconfigurations in Active Directory using ldap3
to identify GenericAll, WriteDACL, and WriteOwner abuse paths
domain: cybersecurity
subdomain: identity-security
tags:
- active-directory
- acl-abuse
- ldap
- privilege-escalation
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- PR.AA-01
- PR.AA-05
- PR.AA-06
mitre_attack:
- T1098
- T1098.007
- T1484.001
- T1222.001
- T1078.002
---
# 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).
4. **Resolve SIDs to principals**: Map security identifiers (SIDs) to human-readable account names using LDAP lookups against the domain. Identify well-known SIDs for built-in groups.
5. **Check for dangerous permissions**: Compare each ACE's access mask against dangerous permission bitmasks: GenericAll (0x10000000), WriteDACL (0x00040000), WriteOwner (0x00080000), GenericWrite (0x40000000), and WriteProperty for specific extended rights.
6. **Filter non-admin trustees**: Exclude expected administrative trustees (Domain Admins, Enterprise Admins, SYSTEM, Administrators) and flag ACEs where non-privileged users or groups hold dangerous permissions.
7. **Map attack paths**: For each finding, document the potential attack chain (e.g., GenericAll on user allows password reset, WriteDACL on group allows adding self to group).
8. **Generate remediation report**: Output a JSON report with all dangerous ACEs, affected objects, non-admin trustees, and recommended remediation steps.
## Expected Output
```json
{
"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"
}
]
}
```Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
6c59587be632full audit observations/trust-audit/skill/mukul975__analyzing-active-directory-acl-abuse.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-07 | 6c59587be632 | SAFE | B | 89 | first audit |
Questions
What does the Analyzing Active Directory Acl Abuse skill do?
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·
Is Analyzing Active Directory Acl Abuse safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Analyzing Active Directory Acl Abuse access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (6c59587be632), read on 2026-10-07. The repository is watched, and a new audit runs when it changes — this is the first audit.