Graphql Introspection in Flask with Dynamodb

GraphQL introspection in a Flask application that uses Amazon DynamoDB can expose both schema details and data access patterns that increase risk. Introspection queries (e.g., __schema { queryType { name } }) are often enabled in development and sometimes left enabled in production. When a Flask GraphQL endpoint serves a DynamoDB-backed resolver, introspection can reveal field names, types, and query names, which an attacker can use to craft targeted BOLA/IDOR or Property Authorization flaws.

In this stack, the schema typically defines types that map to DynamoDB table key structures (partition key, sort key). If authorization checks are applied at the resolver level rather than at the data access layer, introspection may disclose which fields correspond to user-specific keys. An attacker who discovers these field mappings can manipulate queries to request attributes that should be restricted, especially when combined with weak or missing authorization checks. For example, if a resolver uses a DynamoDB GetItem or Query and relies on user input directly from GraphQL arguments to build the key, missing ownership validation can lead to BOLA/IDOR.

Additionally, because middleBrick tests GraphQL endpoints in an unauthenticated, black-box manner, it can detect whether introspection is enabled and whether the schema exposes sensitive query names or types. Findings may highlight missing rate limiting on introspection queries, excessive data exposure through types, or unsafe consumption patterns where user input is passed to DynamoDB without validation. These issues align with the OWASP API Top 10 categories of Broken Object Level Authorization and Excessive Data Exposure, and they can be correlated with findings from the 12 security checks run in parallel.

Using the CLI tool, you can quickly assess this combination by running middlebrick scan <url> against your Flask endpoint. The report will indicate whether introspection is active, whether schema details are exposed, and whether any related authorization findings appear. If you are using the Pro plan, continuous monitoring can keep this risk visible across schema or table structure changes, and the GitHub Action can fail a build if the security score drops below your chosen threshold.

To reduce risk when using GraphQL introspection with DynamoDB in Flask, apply strict authorization, input validation, and schema controls. Ensure that introspection is disabled in production environments and that resolvers validate ownership before querying DynamoDB.

First, disable introspection in production by configuring your GraphQL view in Flask. If you use graphene or strawberry, you can conditionally enable introspection based on an environment variable:

from flask import Flask
from graphene import ObjectType, String, Schema
import os

class Query(ObjectType):
    hello = String(name=String(default_value=''))

    def resolve_hello(self, info, name):
        return f'Hello {name}'

def get_schema():
    enable_introspection = os.environ.get('ENABLE_INTROSPECTION', 'false').lower() == 'true'
    return Schema(query=Query, introspection=enable_introspection)

app = Flask(__name__)
app.config['GRAPHQL_SCHEMA'] = get_schema()

Second, when querying DynamoDB, always derive the partition key from the authenticated user identity and validate that requested item identifiers belong to that user. Avoid using raw user input as the key without checks. The following example shows a resolver that uses the boto3 DynamoDB client safely:

import boto3
from flask import g
from botocore.exceptions import ClientError

dynamodb = boto3.resource('dynamodb')
table_name = 'UserProfiles'

def get_user_profile(user_id, profile_id):
    # user_id is derived from the authenticated session, not from user input
    table = dynamodb.Table(table_name)
    try:
        response = table.get_item(
            Key={
                'user_id': user_id,
                'profile_id': profile_id
            }
        )
        item = response.get('Item')
        if not item or item['user_id'] != user_id:
            return None
        return item
    except ClientError as e:
        return None

Third, apply input validation on all GraphQL arguments that influence DynamoDB requests. Use a validation layer to ensure IDs match expected patterns and to prevent overly broad queries that could trigger excessive data exposure. If you integrate with the middleBrick MCP Server from your IDE, you can run scans during development to catch introspection and authorization issues early. For teams, the Pro plan provides continuous monitoring so changes to your DynamoDB table schema or GraphQL types are evaluated against the same security checks on a schedule.