# Authorization Analysis Report

## 1. Executive Summary

- **Analysis Status:** Complete  
- **Key Outcome:** Five high-confidence authorization vulnerabilities were identified and documented. All findings represent **complete absence of authentication and authorization controls** rather than subtle logic flaws. The application implements ZERO security guards between external HTTP requests and sensitive database operations, enabling trivial privilege escalation and horizontal access control bypass.
- **Purpose of this Document:** This report provides strategic context and architectural intelligence for the Authorization Exploitation phase. The machine-readable exploitation queue (`authz_exploitation_queue.json`) contains actionable vulnerability specifications, while this document explains the dominant patterns, security architecture gaps, and exploitation strategies.

## 2. Dominant Vulnerability Patterns

### Pattern 1: Complete Absence of Authentication (All Vulnerability Types)
- **Description:** The application has ZERO authentication mechanisms. No login endpoints, no session management, no JWT tokens, no password fields in the User model. Every endpoint accepts requests from anonymous users without verifying identity.
- **Implication:** This is not a bypassable authentication system - authentication literally does not exist. All authorization controls are impossible to implement without first establishing identity.
- **Representative:** AUTHZ-VULN-01, AUTHZ-VULN-02, AUTHZ-VULN-03, AUTHZ-VULN-04, AUTHZ-VULN-05
- **Code Evidence:** 
  - No `@login_required` decorators anywhere in codebase
  - No `@app.before_request` authentication hooks (app.py has none)
  - No authentication libraries in requirements.txt (no Flask-Login, Flask-JWT-Extended, Flask-HTTPAuth)
  - User model has no password field (models.py:4-8)

### Pattern 2: Missing Ownership Validation (Horizontal)
- **Description:** The GraphQL resolver `resolve_users()` starts with `User.objects()` which returns ALL users from the database. No filtering by current user ID, no checks to ensure users can only access their own data.
- **Implication:** Even if authentication existed, the resolver would still return all users because there's no `query.filter(user_id=current_user.id)` logic.
- **Representative:** AUTHZ-VULN-01, AUTHZ-VULN-05
- **Code Evidence:** schema.py:9 - `query = User.objects()` with no subsequent ownership filter

### Pattern 3: Missing Role-Based Access Control (Vertical)
- **Description:** The User model contains an `is_admin` boolean field (models.py:7) but this field is NEVER checked for authorization purposes. The GraphQL resolver accepts an `is_admin` parameter but completely ignores it in the function body.
- **Implication:** Admin-only resources like the `flag` field and admin user enumeration are accessible to all users because no code ever validates `if current_user.is_admin`.
- **Representative:** AUTHZ-VULN-02, AUTHZ-VULN-03
- **Code Evidence:** 
  - schema.py:8 - `is_admin=None` parameter accepted but never used in lines 9-29
  - No role checks anywhere: searched for "is_admin" in authorization context - ZERO results

### Pattern 4: Field-Level Authorization Failure (Vertical)
- **Description:** Sensitive fields like `flag` are exposed in the GraphQL UserType schema (models.py:13) with NO field-level permission checks. No custom resolvers (`resolve_flag()`), no decorators, no conditional exposure.
- **Implication:** The flag field containing the CTF secret is returned in GraphQL responses to any requester who includes it in their query.
- **Representative:** AUTHZ-VULN-02
- **Code Evidence:** models.py:10-13 - UserType exposes username, email, and flag with no access controls

### Pattern 5: Development Tools in Production (Vertical)
- **Description:** GraphiQL interactive IDE is enabled in production (`graphiql=True` at app.py:16) with no authentication requirements, allowing anonymous users to explore the entire API schema, discover hidden fields, and test attacks interactively.
- **Implication:** Attackers gain reconnaissance capabilities equivalent to internal developers, including schema introspection revealing the `flag` field and all query parameters.
- **Representative:** AUTHZ-VULN-04
- **Code Evidence:** app.py:16 - `graphiql=True` with no guards on /graphql route

## 3. Strategic Intelligence for Exploitation

### Session Management Architecture:
- **Finding:** NO SESSION MANAGEMENT EXISTS
- **Details:** Flask app initialized with default configuration only (app.py:8). No SECRET_KEY, no session cookies, no JWT tokens. The application is completely stateless from an identity perspective.
- **Critical Finding:** Since sessions don't exist, there's nothing to hijack or forge. Authentication must be bypassed by... not existing.

### Role/Permission Model:
- **Database Schema:** Two roles exist in the data model:
  - Regular users: `is_admin=False` (3 users: user1, user2, user3)
  - Admin users: `is_admin=True` (2 users: admin, admin2)
- **Flag Location:** CTF flag stored only in `admin2` user document (app.py:44)
- **Critical Finding:** The `is_admin` field exists in the database but is NEVER checked in application logic. It's purely decorative data with zero security enforcement.

### Resource Access Patterns:
- **Entry Points:** Three network-accessible endpoints all leading to the same vulnerable resolver:
  1. `POST /api/users` - REST wrapper around GraphQL (app.py:24-38)
  2. `GET/POST /graphql` - Direct GraphQL endpoint (app.py:11-18)
  3. `GET /` - Web UI that calls /api/users via AJAX (index.html)
- **Data Flow:** HTTP Request → Flask routing → schema.execute(query) → resolve_users() → User.objects() → MongoDB
- **Critical Finding:** ZERO security checks at any stage. No decorators on routes, no middleware, no resolver-level guards, no database-level filtering.

### GraphQL Parameter Exploitation:
- **search parameter:** Accepts JSON string, deserialized via `json.loads()`, unpacked into MongoDB filter with `**search_criteria` (schema.py:13-14)
  - **Exploit:** Inject arbitrary MongoDB operators: `{"is_admin": true}`, `{"flag": {"$ne": null}}`
  - **No Validation:** No whitelist of allowed fields, no blacklist of dangerous operators
- **options parameter:** Accepts JSON for pagination (skip, limit) with no bounds checking (schema.py:19-27)
  - **Exploit:** Can specify `{"limit": 999999}` to retrieve all users in single request
- **is_admin parameter:** Accepted in GraphQL schema but completely unused in resolver
  - **False Security:** This parameter's existence might mislead developers into thinking authorization exists
- **dummy parameter:** Accepted but never processed - dead code (schema.py:8)

### Network Accessibility:
- **Host Binding:** Flask configured with `host='0.0.0.0'` (app.py:53) - binds to ALL network interfaces
- **Port Exposure:** Docker Compose maps 5003:5003 (docker-compose.yml:6-7) - externally accessible
- **Protocol:** HTTP only, no HTTPS/TLS
- **External Exploitability:** YES - all vulnerabilities are exploitable from the public internet without VPN or internal network access

## 4. Vectors Analyzed and Confirmed Secure

**STATUS: NO SECURE AUTHORIZATION IMPLEMENTATIONS FOUND**

This section is intentionally empty because the application implements ZERO authorization controls. There are no endpoints with proper guards, no middleware with correct authentication, and no resolvers with ownership validation.

| **Endpoint** | **Guard Location** | **Defense Mechanism** | **Verdict** |
|--------------|-------------------|----------------------|-------------|
| N/A | N/A | N/A | All endpoints vulnerable |

**Note:** The recon deliverable's section 6.4 "Guards Directory" correctly states: "NO GUARDS EXIST - All endpoints are publicly accessible without authentication or authorization."

## 5. Analysis Constraints and Blind Spots

### Architectural Constraints:
- **CTF Challenge Context:** This application is intentionally vulnerable as a Capture The Flag challenge. The complete absence of security controls is by design, not oversight. In a real-world penetration test, this level of vulnerability would be extraordinarily rare.

### Analysis Limitations:
- **Static Analysis Only:** This analysis is based entirely on source code review. The exploitation phase will confirm these vulnerabilities through live testing, but given the complete absence of guards, there is near-zero uncertainty.
- **No Runtime Behavior:** Dynamic authorization systems (e.g., database-driven permissions, policy engines) could theoretically exist outside the codebase, but file analysis shows no such integrations (no imports of policy libraries, no external service calls).

### Unverified Assumptions:
- **MongoDB Configuration:** The analysis assumes MongoDB has no server-side authorization rules or triggers. The database connection is plaintext `mongodb://mongo:27017/appdb` with no authentication (database.py:5-6), but MongoDB could theoretically enforce collection-level permissions. However, the docker-compose.yml shows MongoDB launched with no `--auth` flag, confirming no database-level authorization.
- **Reverse Proxy:** The analysis assumes no reverse proxy (nginx, Apache) sits in front of the Flask app enforcing authentication. The Docker configuration shows Flask serving directly on port 5003 with no infrastructure layer.

### Out of Scope:
- **Network-Level Controls:** The application may be deployed behind a firewall or VPN in production, but the scope specifies analysis of the application code itself, not network infrastructure.
- **Container Isolation:** Docker container security (seccomp profiles, AppArmor) is out of scope for authorization analysis.

## 6. Architectural Weaknesses

### Trust Boundary Analysis:
- **Zero Trust Boundaries:** The application treats all incoming requests as equally trustworthy. There is no distinction between:
  - Public vs. authenticated endpoints
  - User vs. admin operations  
  - Own-data vs. other-users-data access
- **Single Layer:** No defense-in-depth. If one layer fails (which it does - no authentication), there are no secondary controls.

### Code Architecture Issues:
- **No Separation of Concerns:** Authorization logic doesn't exist as a separate module that could be audited or tested independently.
- **Implicit Trust:** The resolver assumes all GraphQL queries are legitimate because there's no concept of "illegitimate" without identity verification.
- **Field Exposure:** The GraphQL schema exposes all User model fields without a whitelist/blacklist approach for sensitive data.

### Deployment Configuration:
- **Debug Mode in Production:** `debug=True` (app.py:53) enables Werkzeug debugger, compounding authorization failures with information disclosure.
- **Development Tools Exposed:** GraphiQL interface is production-accessible, providing attack surface mapping tools to adversaries.

## 7. Exploitation Difficulty Assessment

**Overall Difficulty: TRIVIAL**

All identified vulnerabilities require:
- **Authentication:** NONE
- **Special Privileges:** NONE  
- **Technical Skill:** LOW (basic HTTP requests or web browser)
- **Tools Required:** curl, web browser, or any HTTP client
- **Time to Exploit:** < 1 minute per vulnerability

**Exploitation Complexity Factors:**
- **No CAPTCHA:** No rate limiting or bot detection
- **No IP Restrictions:** Externally accessible from any source IP
- **No WAF:** No Web Application Firewall signatures to evade
- **No Logging:** Minimal logging (single print statement at app.py:36) means attacks are unlikely to be detected

**Example Exploitation Timeline:**
1. **Minute 0:** Discover GraphiQL at /graphql
2. **Minute 1:** Run introspection query to find `flag` field
3. **Minute 2:** Query for admin users: `users(search: "{\"is_admin\": true}") { username }`
4. **Minute 3:** Extract flag: `users(search: "{\"username\": \"admin2\"}") { flag }`
5. **Result:** CTF flag obtained in under 5 minutes with zero obstacles

## 8. Comparison to Security Best Practices

**Best Practice: Defense in Depth**
- **Current State:** Single layer (application), and that layer has zero controls
- **Expected:** Network firewall → WAF → Application auth → Database permissions

**Best Practice: Least Privilege**
- **Current State:** All users (including anonymous) have maximum privilege
- **Expected:** Role-based access with minimal necessary permissions per role

**Best Practice: Secure by Default**
- **Current State:** GraphiQL enabled, debug mode on, introspection enabled
- **Expected:** Production-hardened defaults, development tools disabled

**Best Practice: Field-Level Security**
- **Current State:** All fields exposed in GraphQL schema
- **Expected:** Sensitive fields (flag) should have custom resolvers with permission checks

**Best Practice: Input Validation**
- **Current State:** `json.loads()` with `**kwargs` unpacking enables NoSQL injection
- **Expected:** Whitelist of allowed search fields, sanitization of operators

## 9. Recommendations for Remediation

**CRITICAL Priority (Must Fix Before ANY Production Use):**

1. **Implement Authentication:**
   - Add Flask-Login or Flask-JWT-Extended
   - Create login/logout endpoints
   - Add password field to User model with bcrypt hashing
   - Require authentication on ALL endpoints via `@login_required` decorator

2. **Implement Authorization:**
   - Add ownership check in `resolve_users()`: `query.filter(user_id=current_user.id)`
   - Add role check for admin-only fields: `if not current_user.is_admin: raise Unauthorized()`
   - Create custom `resolve_flag()` method with admin role verification

3. **Disable Development Tools:**
   - Set `graphiql=False` in production
   - Set `debug=False` in Flask configuration
   - Disable schema introspection: `schema = graphene.Schema(query=Query, introspection=False)`

4. **Input Validation:**
   - Whitelist allowed search fields: `ALLOWED_FIELDS = ['username', 'email']`
   - Reject MongoDB operators: Sanitize `$` characters from search input
   - Use parameterized queries instead of `**kwargs` unpacking

5. **Field-Level Authorization:**
   - Remove `flag` field from UserType or add custom resolver
   - Implement field-level permissions using GraphQL middleware
   - Create separate admin-only GraphQL schema/endpoints

**HIGH Priority:**
- Add rate limiting (Flask-Limiter) to prevent brute force
- Implement audit logging for all data access
- Add security headers (CSP, HSTS, X-Frame-Options)
- Enable MongoDB authentication with user/password
- Use HTTPS/TLS instead of HTTP

## 10. Conclusion

This authorization analysis identified **five distinct authorization vulnerabilities**, all stemming from a single architectural flaw: **the complete absence of authentication and authorization mechanisms**. 

The application is not "poorly secured" - it is **intentionally unsecured** as a CTF challenge. Every vulnerability has **high confidence** because the missing guards are definitively absent from the codebase through exhaustive static analysis.

All vulnerabilities are **externally exploitable** from the public internet without any special access, making them appropriate for the external attacker scope. The exploitation queue provides actionable specifications for the next phase to confirm these findings through live testing.

**Risk Assessment:** If this were a production application (it's not - it's a CTF), the risk level would be **CATASTROPHIC** with immediate recommendation to take the application offline until fundamental security controls are implemented.