SKILLS/analyzing-active-directory-acl-abuse/SKILL.md
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
npx skillsauth add pinkpixel-dev/skills-collection-1 analyzing-active-directory-acl-abuseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
pip install ldap3)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.
{
"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"
}
]
}
testing
When the user wants a full ASO health audit, review their App Store listing quality, or diagnose why their app isn't ranking. Also use when the user mentions "ASO audit", "ASO score", "why am I not ranking", "listing review", or "optimize my app store page". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
testing
Clarify requirements before implementing. Use when serious doubts arise.
tools
Complete reference and build guide for ASI:One (ASI1) — the AI platform by Fetch.ai built for agentic, Web3-native applications. Use this skill IMMEDIATELY and ALWAYS when the user mentions ASI1, ASI:One, Fetch.ai AI API, building with ASI1, integrating ASI:One, asking about ASI1 models, tool calling with ASI1, ASI1 image generation, ASI1 agentic LLM, Agentverse, uagents, Agent Chat Protocol, structured output with ASI1, or OpenAI-compatible wrappers for ASI1. Also trigger when the user says things like "use ASI1 instead of OpenAI", "build an app with ASI:One", "ASI1 API", or references docs.asi1.ai. This skill covers everything needed to build production apps - setup, all models, all API features, tool calling, image gen, agentic orchestration, structured data, session management, streaming, LangChain integration, uagents / Agent Chat Protocol, and TypeScript/Node.js patterns.
data-ai
When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.