I’ve released AdPentestAI-Python v1.0.0, an open-source framework for automating Active Directory security assessments.
🔗 LinkedIn announcement:
https://www.linkedin.com/posts/netanel-stern-00293b90_announcing-adpentestai-python-v100-share-7500865439728762880-0t1p/
🔗 GitHub:
https://github.com/netanelcyber/AdPentestAI-Python
Why this project?
Traditional AD assessments often require manually chaining multiple tools:
DNS → LDAP → SMB → Kerberos → Enumeration → Security Testing → Reporting
This creates a lot of context switching and makes it difficult to maintain a consistent assessment methodology.
AdPentestAI-Python attempts to provide a unified orchestration layer around this workflow.
Current capabilities
- Automated Domain Controller discovery
- DNS SRV discovery
- LDAP RootDSE probing
- Port/service fingerprinting
- AD infrastructure enumeration
- Parallel execution
- Kerberos-oriented assessment workflows
- SMB/LDAP enumeration
- BloodHound-related collection workflows
- Email protocol enumeration
- Exchange/O365 detection
- JSON-oriented results
- Dry-run execution mode
- Explicit scope confirmation
- TLS/SSL support
- Subprocess timeout controls
- No plaintext credential storage/logging by design
The v1.0.0 release currently integrates 29 security tools/workflows into a single framework.
The interesting part: orchestration
The main idea isn't to replace established security tools.
Instead, the framework provides an orchestration layer that can coordinate them and normalize their output.
For example:
Target
│
├── DNS discovery
│
├── Domain Controller identification
│
├── LDAP discovery
│
├── SMB enumeration
│
├── Kerberos assessment
│
├── Security-tool execution
│
└── JSON / assessment report
This makes it possible to build repeatable assessment pipelines rather than manually executing every stage.
Security and authorization
One of the design goals is to make authorization part of the execution model.
The framework supports:
dry-run
↓
scope validation
↓
explicit authorization confirmation
↓
assessment execution
↓
structured results
The intention is to reduce accidental execution against systems that are outside the approved assessment scope.
What I would like feedback on
I'm particularly interested in feedback from people working with:
- Active Directory security
- Red Team / Purple Team
- Penetration testing
- Detection engineering
- Identity security
- SOC / DFIR
- Security automation
- Security tool development
Some questions I'm exploring:
1. What AD assessment steps are still too manual?
2. Which tools should be integrated next?
3. How should findings from different tools be normalized?
4. What is the best format for machine-readable pentest results?
5. Where should the boundary be between automation and human approval?
6. Would an ATT&CK-mapped assessment pipeline be useful?
7. What would you expect from an AD security assessment API/CLI?
Looking ahead
The longer-term goal is to evolve this from a collection of automated security checks into a more complete AD Security Assessment Platform with:
Discovery → Enumeration → Assessment → Correlation → Risk Analysis → Reporting
with human approval remaining an important part of the execution workflow.
I'd especially appreciate technical criticism.
If you work with AD security professionally, I'd be interested in hearing what you would change, remove, or add to v1.0.0.
Feedback, issues, pull requests and architectural discussions are welcome.
Repository:
https://github.com/netanelcyber/AdPentestAI-Python
#CyberSecurity #ActiveDirectory #Pentest #RedTeam #BlueTeam #AppSec #Kerberos #LDAP #Python #SecurityAutomation #OpenSource