A Python-driven automation and notification engine featuring secure, scalable, and concurrent job execution with real-world security assurance.
flowchart TB
%% Batch Orchestrator
BO["Batch Orchestrator\n(batch_scan.py, cron/CLI)"]
style BO fill:#158aff,color:#fff,stroke:#0055aa,stroke-width:2px
%% Scanners (grouped, but laid out vertically)
S1a["Subdub.py\nSubdomain Discovery"]
S1b["Filezer.py\nFile Discovery"]
S1c["Panelz.py\nAdmin Panel Finder"]
S1d["Cveez.py\nCVE Scan"]
S1e["Cnamer.py\nTakeover Checks"]
S1f["Hoster2.py\nHost/Service Enum"]
S1g["Miscon.py\nMisconfiguration Scan"]
style S1a fill:#e658ea,color:#fff,stroke:#9800a1,stroke-width:2px
style S1b fill:#e658ea,color:#fff,stroke:#9800a1,stroke-width:2px
style S1c fill:#e658ea,color:#fff,stroke:#9800a1,stroke-width:2px
style S1d fill:#e658ea,color:#fff,stroke:#9800a1,stroke-width:2px
style S1e fill:#e658ea,color:#fff,stroke:#9800a1,stroke-width:2px
style S1f fill:#e658ea,color:#fff,stroke:#9800a1,stroke-width:2px
style S1g fill:#e658ea,color:#fff,stroke:#9800a1,stroke-width:2px
%% Worker Pools (each script has its own worker pool below it)
TP1["Worker Pool\n(10 workers)"]
TP2["Worker Pool\n(10 workers)"]
TP3["Worker Pool\n(10 workers)"]
TP4["Worker Pool\n(15 workers)"]
TP5["Worker Pool\n(5 workers)"]
TP6["Worker Pool\n(10 workers)"]
TP7["Worker Pool\n(8 workers)"]
style TP1 fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
style TP2 fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
style TP3 fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
style TP4 fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
style TP5 fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
style TP6 fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
style TP7 fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
%% Data / Output
DT["Datatables & Files\n(.csv/.json)"]
style DT fill:#f7f323,color:#000,stroke:#888800,stroke-width:2px
%% Database
DB["MySQL Database\nFindings & Claims"]
style DB fill:#ff9514,color:#fff,stroke:#b55000,stroke-width:3px
%% Slack Notifier
SLK["Slack Bot\nReports & Alerts"]
style SLK fill:#00eaea,color:#222,stroke:#008888,stroke-width:2px
%% Azure Takeover
AZ["Azure Automation\nTakeover Evidence"]
style AZ fill:#9c60ff,color:#fff,stroke:#370099,stroke-width:2px
%% Test Automation
T1["Unit Tests"]
T2["Concurrency Tests"]
T3["Integration Tests"]
style T1 fill:#333,color:#fff,stroke:#fff,stroke-width:2px
style T2 fill:#333,color:#fff,stroke:#fff,stroke-width:2px
style T3 fill:#333,color:#fff,stroke:#fff,stroke-width:2px
%% Flows (VERTICAL)
BO --> S1a --> TP1 --> DT
BO --> S1b --> TP2 --> DT
BO --> S1c --> TP3 --> DT
BO --> S1d --> TP4 --> DT
BO --> S1e --> TP5 --> DT
BO --> S1f --> TP6 --> DT
BO --> S1g --> TP7 --> DT
DT --> DB
S1a --Results--> DB
S1b --Results--> DB
S1c --Results--> DB
S1d --Results--> DB
S1e --Results/Claims--> DB
S1f --Results--> DB
S1g --Results--> DB
S1a --Notify--> SLK
S1b --Notify--> SLK
S1c --Notify--> SLK
S1d --Notify--> SLK
S1e --Notify--> SLK
S1f --Notify--> SLK
S1g --Notify--> SLK
S1e --Claim Workflow--> AZ
%% Test coverage links (dashed, indirect)
T1 -.-> S1a
T2 -.-> TP1
T3 -.-> S1e
Key Components:
- Job Scheduler: Handles all cron-like and ad-hoc tasks using
scheduleorAPScheduler. - Worker Pool: Manages concurrency via Python's
ThreadPoolExecutor, processing jobs from a thread-safe queue. - Datatables & MySQL Storage:
- Datatables: In-memory data manipulation (e.g., with Pandas or custom classes).
- MySQL: Reliable persistent storage for results, job configs, and logs.
- Slack Integration: Automated timely notifications using
slack_sdk. - Extensibility: Designed for additional integrations and scaling.
- Testing:
- Unit: Mocks for DB and Slack.
- Integration/E2E: Covers full flows across components.
- Threading/Performance: Stress tests for race conditions and stability.
Universal Authentication Vulnerability Discovery Platform - Scan any authentication library, discover vulnerabilities, and generate exploitable demonstrations using exact vulnerable code patterns.
flowchart TB
%% Entry Point
Main["universal_auth_scanner.py
Main Scanner Entry Point"]
style Main fill:#158aff,color:#fff,stroke:#0055aa,stroke-width:2px
%% Repository Analysis
Clone["Repository Cloner
GitHub/GitLab Integration"]
style Clone fill:#e658ea,color:#fff,stroke:#9800a1,stroke-width:2px
%% AI Analysis Engine
AI["Azure GPT-5 Engine
Library Understanding"]
style AI fill:#9c60ff,color:#fff,stroke:#370099,stroke-width:2px
%% Vulnerability Detection
Scan["Vulnerability Scanner
Auth-Specific Patterns"]
Extract["Code Pattern Extractor
Exact Vulnerable Code"]
style Scan fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
style Extract fill:#2fd05c,color:#fff,stroke:#0e6626,stroke-width:2px
%% Demo Generation
Service["Vulnerable Service Generator
Identical Code Implementation"]
Exploit["Targeted Exploit Generator
Working Attack Scripts"]
Demo["Demo Package Assembly
Complete Testing Environment"]
style Service fill:#ff9514,color:#fff,stroke:#b55000,stroke-width:2px
style Exploit fill:#ff9514,color:#fff,stroke:#b55000,stroke-width:2px
style Demo fill:#ff9514,color:#fff,stroke:#b55000,stroke-width:2px
%% Output
Report["Business Impact Assessment
Team-Ready Exploits"]
style Report fill:#f7f323,color:#000,stroke:#888800,stroke-width:2px
%% Flow
Main --> Clone
Clone --> AI
AI --> Scan
Scan --> Extract
Extract --> Service
Extract --> Exploit
Service --> Demo
Exploit --> Demo
Demo --> Report
Core Authentication Security Components:
- Universal Repository Scanner: Automatically clones and analyzes any authentication library (OAuth, JWT, SAML, Basic Auth, API Keys) from GitHub, GitLab, or other Git repositories.
- AI-Powered Analysis Engine: Uses Azure GPT-5 to understand library purpose, identify authentication flows, and predict context-specific vulnerabilities beyond simple pattern matching.
- Vulnerability Pattern Detection: Specialized scanning for authentication-specific issues—hardcoded secrets, weak token validation, insecure flows, missing security controls.
- Exact Code Extraction: Extracts actual vulnerable code patterns from repositories and implements identical structures in demonstration services.
- Targeted Exploit Development: Creates working exploits that specifically target the vulnerable patterns found, enabling teams to test their own implementations.
- Enterprise Demo Generation: Produces complete testing packages with Docker services, exploitation scripts, and business impact documentation.
mov37 specializes in discovering real-world authentication vulnerabilities with immediate business impact:
| Vulnerability Type | Detection Method | Real-World Impact |
|---|---|---|
| Hardcoded Credentials | Pattern matching + AI analysis | Direct authentication bypass |
| ROPC Vulnerabilities | OAuth flow analysis | Password spraying attacks |
| JWT Security Issues | Token validation testing | Session hijacking |
| OAuth Flow Manipulation | State/redirect validation | Account takeover |
| Weak Client Authentication | Implementation analysis | API access control bypass |
| Session Management Flaws | Token lifecycle testing | Privilege escalation |
Microsoft MSAL.js Vulnerability Discovery:
🚨 VULNERABILITY FOUND: PASSWORD_GRANT="password" pattern
Location: msal-browser.min.js:2
Risk: CRITICAL - Direct authentication bypass
Pattern: MSALPasswordGrantHandler with hardcoded credentials
✅ DEMO GENERATED: msal_vulnerability_demo/
Service: Uses EXACT vulnerable code from Microsoft's library
Exploit: Working password spraying attacks
Business Impact: Teams can test same vulnerability in their MSAL.js implementationsEnterprise Authentication Testing Results:
| Authentication Library | Vulnerabilities Found | Exploits Generated | Business Impact |
|---|---|---|---|
| Microsoft MSAL.js | ROPC + Hardcoded secrets | ✅ Working demonstrations | HIGH - Credential attacks |
| Authlib (Python) | Client secret exposure | ✅ OAuth token manipulation | MEDIUM - API access |
| Spring Security | Session fixation | ✅ Privilege escalation | HIGH - Admin bypass |
- Core Platform: Python 3.9+, Git integration, Docker containerization
- AI Analysis: Azure GPT-5 integration for intelligent library assessment
- Vulnerability Detection: Authentication-specific pattern matching and analysis
- Code Replication: Exact vulnerable pattern extraction and implementation
- Exploit Generation: Flask services, targeted attack scripts, Docker composition
- Enterprise Integration: CI/CD ready, business-focused reporting
Security Team Authentication Assessment
- Automated vulnerability discovery in authentication libraries used by organization
- Proof-of-concept exploit generation for discovered vulnerabilities
- Concrete evidence of authentication security risks for business stakeholders
Development Team Security Validation
- Pre-deployment authentication security testing with working exploits
- Vulnerable code pattern identification in existing authentication implementations
- Security training using real-world authentication vulnerability demonstrations
Penetration Testing & Red Team Operations
- Authentication-focused security testing with immediately usable exploits
- OAuth, JWT, SAML vulnerability discovery and practical demonstration
- Authentication bypass and privilege escalation testing frameworks
Unlike theoretical security research, mov37 creates demonstrations using the exact same vulnerable code patterns found in the original repositories:
What makes this valuable:
- Teams can run the same exploits against their own authentication implementations
- Validates whether production systems have identical vulnerabilities
- Provides concrete proof that specific code patterns are exploitable
- Enables immediate testing of discovered vulnerabilities in real environments
- Repository: github.com/fitzpr/mov37
- Documentation: Comprehensive authentication security testing guide
- License: MIT License
- Specialization: Authentication & Authorization Security Research