analyzing-active-directory-acl-abuse skill

by mukul975·mukul975/Anthropic-Cybersecurity-Skills·34k stars·Apache-2.0

Detect dangerous ACL misconfigurations in Active Directory using ldap3

A100/100content scan

Is the analyzing-active-directory-acl-abuse skill safe?

Clean: nothing in its files matched our rules. We read 4 files in the folder on 2026-09-28.

No findings.

Install the analyzing-active-directory-acl-abuse skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p ~/.claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-active-directory-acl-abuse ~/.claude/skills/analyzing-active-directory-acl-abuse

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it 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, and sAMAccountName attributes.

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).

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.

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.

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.

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).

Generate remediation report: Output a JSON report with all dangerous ACEs, affected objects, non-admin trustees, and recommended remediation steps.

Expected Output

{
  "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"
    }
  ]
}

More skills from mukul975/Anthropic-Cybersecurity-Skills

Aabusing-dpapi-for-credential-accessExtract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket's dpapi.py, including domain-wide decryption via the DPAPI backup key. Use during authorized red-team credential-access engagements after gaining a foothold or when triaging DPAPI blobs pulled from a host.
Aabusing-shadow-credentials-for-privescTake over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target's NT hash without a password reset. Use when BloodHound shows GenericWrite/GenericAll/AddKeyCredentialLink over a target, as a stealthier alternative to ForceChangePassword, during authorized red-team engagements.
Aachieving-cmmc-level-2-compliancePrepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
Aacquiring-disk-image-with-dd-and-dcflddCreate forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.
Aanalyzing-android-malware-with-apktoolPerform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.
Danalyzing-api-gateway-access-logs'Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect
Aanalyzing-apt-group-with-mitre-navigatorQuery ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator visualizations for threat-intel reporting.
Aanalyzing-azure-activity-logs-for-threats'Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query
Aanalyzing-bootkit-and-rootkit-samples'Analyzes bootkit and advanced rootkit malware infecting the Master
Aanalyzing-browser-forensics-with-hindsightParse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user's web activity from a browser profile.
Aanalyzing-campaign-attribution-evidenceSystematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.
Aanalyzing-certificate-transparency-for-phishingMonitor Certificate Transparency logs using crt.sh and Certstream to

All agent skills → · MCP servers