Token Leakage in Flask with Dynamodb

Token leakage in a Flask application that uses DynamoDB often occurs when session or authentication tokens are mishandled in code that interacts with DynamoDB. Because Flask does not enforce strict separation between application logic and data access patterns, developers may inadvertently expose tokens through logs, error messages, or insecure serialization.

DynamoDB stores data as JSON-like documents, and tokens are sometimes stored directly in item attributes for session management or user context. If these items are retrieved without proper filtering, tokens can be returned in responses or exposed through misconfigured queries. For example, a query that scans a table to fetch user data might return a token attribute if the projection expression or filter is not explicitly limited to non-sensitive fields.

Another vector is error handling. Flask routes that catch DynamoDB exceptions and return detailed error objects may include token values present in request parameters or item structures. The use of unvalidated user input in key expressions, such as user_id in get_item, can lead to unintended data retrieval that includes token attributes.

Consider a route that fetches a user profile and returns it as JSON. If the DynamoDB item contains a token field and the response serialization does not exclude it, the token can be sent to the client. Additionally, logging of DynamoDB responses in Flask debugging or monitoring tools can persist tokens in plaintext if log levels are set to DEBUG and responses are fully dumped.

Compliance mappings such as OWASP API Top 10 highlight broken object level authorization (BOLA) as a common risk when tokens are exposed through improper data access controls. In PCI-DSS and SOC2 contexts, exposure of authentication tokens may violate requirements for protecting credential data. middleBrick scans for these patterns by correlating OpenAPI definitions with runtime behavior, flagging endpoints that return sensitive attributes like tokens without explicit authorization checks.

To prevent token leakage, limit what DynamoDB returns to only necessary attributes and avoid exposing tokens in logs or error responses. Use projection expressions in queries and scans to restrict returned fields, and ensure token fields are never included in responses sent to clients.

Below are concrete code examples for secure DynamoDB usage in Flask.

1. Query with Projection Expression

Use ProjectionExpression to return only non-sensitive attributes.

import boto3
from flask import Flask, jsonify, request

app = Flask(__name__)
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('users')

@app.route('/profile')
def get_profile():
    user_id = request.args.get('user_id')
    response = table.get_item(
        Key={'user_id': user_id},
        ProjectionExpression='user_id, email, created_at'
    )
    item = response.get('Item')
    if item:
        return jsonify({'user_id': item['user_id'], 'email': item['email'], 'created_at': item['created_at']})
    return jsonify({'error': 'not found'}), 404

2. Scan with Filter and Exclude Tokens

Avoid scanning entire tables; if necessary, filter and exclude sensitive attributes.

import boto3
from flask import Flask, jsonify

app = Flask(__name__)
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('users')

@app.route('/search')
def search_users():
    response = table.scan(
        FilterExpression='attribute_exists(user_id) AND token_not_exists()',
        ProjectionExpression='user_id, email'
    )
    users = [{'user_id': u['user_id'], 'email': u['email']} for u in response.get('Items', [])]
    return jsonify(users)

Note: DynamoDB does not support a native token_not_exists function; you would define a condition expression based on your schema, for example checking that an attribute is absent using attribute_not_exists(token) in a filter.

3. Safe Error Handling

Do not return raw DynamoDB errors that may contain tokens. Sanitize exception responses.

from botocore.exceptions import ClientError
from flask import Flask, jsonify

app = Flask(__name__)

@app.route('/data')
def get_data():
    try:
        # Assume safe query logic here
        pass
    except ClientError as e:
        app.logger.error('DynamoDB error: %s', e.response['Error']['Code'])
        return jsonify({'error': 'internal server error'}), 500

Logging should exclude request parameters and item contents that may contain tokens. Use structured logging that redacts sensitive fields.

4. Environment and IAM Controls

Ensure IAM policies follow least privilege so that tokens stored in DynamoDB cannot be read by services that do not need them. Rotate any exposed credentials and use AWS KMS encryption at rest.

middleBrick can be used to validate these controls by scanning your endpoints and verifying that token-related attributes are not returned without authorization. The scanner checks for missing projection expressions and insecure error handling patterns that could lead to token leakage.