Pii Leakage in Flask with Dynamodb
Pii Leakage in Flask with Dynamodb — how this specific combination creates or exposes the vulnerability
A Flask application that uses Amazon DynamoDB as a backend data store can unintentionally expose personally identifiable information (PII) through several common patterns. PII leakage in this context means sensitive data such as email addresses, names, phone numbers, or government identifiers are returned to callers without appropriate authorization, validation, or masking.
One root cause is incomplete or missing attribute-level filtering when querying DynamoDB. For example, a developer might use a Scan operation or a query with a broad projection expression that returns all attributes, including fields that should be restricted. In Flask, route handlers often directly serialize the DynamoDB response (e.g., a JSON-compatible dict) and return it to the client. Without explicit field selection or redaction, PII-bearing attributes are exposed in the HTTP response.
Another contributing factor is weak identity-based authorization combined with DynamoDB’s key-based access model. If an endpoint uses a user identifier such as user_id from an incoming request to construct a DynamoDB key (partition key or sort key) but does not verify that the authenticated subject is allowed to access that specific item, an insecure direct object reference (IDOR) or broken object level authorization (BOLA) occurs. This enables attackers to enumerate or retrieve other users’ PII by altering identifiers in API requests.
DynamoDB data modeling can also increase risk. Denormalized designs commonly store PII alongside non-sensitive metadata in the same item. If an application retrieves an item for read-only purposes (e.g., profile display) but the item contains sensitive fields such as email, phone_number, or ssn, and those fields are not masked or omitted, the data is effectively leaked over the API.
Logging, monitoring, or error handling in Flask can inadvertently amplify exposure. If exception messages or debug responses include full DynamoDB item dumps, PII can be written to logs or exposed in browser developer tools. Misconfigured CORS or missing content security policies in the Flask app can further widen the leakage surface to unintended origins.
To detect these issues, middleBrick scans the unauthenticated and authenticated attack surface of a Flask+DynamoDB API, checking for missing authorization on object-level endpoints, overly broad data returns, and exposure of sensitive fields in responses. Findings include severity ratings and remediation steps to reduce PII leakage risk.
Dynamodb-Specific Remediation in Flask — concrete code fixes
Remediation focuses on minimizing data exposure, enforcing authorization, and structuring DynamoDB interactions safely within Flask routes.
1. Use projection expressions and select only required fields
Instead of retrieving all attributes, explicitly request only the fields you need. This prevents PII from being returned inadvertently.
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')
if not user_id:
return jsonify({'error': 'missing user_id'}), 400
response = table.get_item(
Key={'user_id': user_id},
ProjectionExpression='user_id, display_name, avatar_url'
)
item = response.get('Item')
if not item:
return jsonify({'error': 'not found'}), 404
return jsonify(item)
2. Enforce ownership-based authorization
Ensure that a user can only access their own items. Never rely solely on client-supplied identifiers without validating ownership.
from flask import g
def user_owns_item(user_id, item_id):
# Implement your own mapping check, e.g., via a secondary index or relationship table
return user_id == item_id # simplified example
@app.route('/users/<string:item_id>')
def get_user_item(item_id):
requester_id = getattr(g, 'user_id', None)
if not requester_id or not user_owns_item(requester_id, item_id):
return jsonify({'error': 'forbidden'}), 403
response = table.get_item(
Key={'user_id': item_id},
ProjectionExpression='user_id, display_name, email'
)
item = response.get('Item')
if not item:
return jsonify({'error': 'not found'}), 404
# Mask or drop sensitive fields before returning
safe_item = {
'user_id': item['user_id'],
'display_name': item['display_name'],
'email': item.get('email', '****') # redacted example
}
return jsonify(safe_item)
3. Conditional writes and update expressions to limit PII changes
When updating items, use UpdateExpression to modify only intended fields and avoid accidentally overwriting or exposing PII.
@app.route('/profile', methods=['POST'])
def update_profile():
user_id = request.json.get('user_id')
display_name = request.json.get('display_name')
if not user_id or not display_name:
return jsonify({'error': 'bad request'}), 400
table.update_item(
Key={'user_id': user_id},
UpdateExpression='SET display_name = :name',
ExpressionAttributeValues={':name': display_name},
ReturnValues='UPDATED_NEW'
)
return jsonify({'status': 'updated'})
4. Secure logging and error handling
Ensure logs do not contain full PII. Avoid printing raw DynamoDB responses in Flask debug output.
import logging
logger = logging.getLogger(__name__)
@app.errorhandler(Exception)
def handle_error(e):
logger.warning('API error: %s', e, exc_info=False) # avoid logging full item
return jsonify({'error': 'internal server error'}), 500
5. Use IAM policies and fine-grained access controls
Configure DynamoDB resource policies and IAM roles to restrict read/write access to specific attributes where possible. While this is not Flask code, it complements application-level controls by reducing the blast radius if a route is misconfigured.
By combining projection expressions, strict authorization checks, and careful data handling, you can significantly reduce PII leakage risks in Flask applications backed by DynamoDB.
Related CWEs: dataExposure
| CWE ID | Name | Severity |
|---|---|---|
| CWE-200 | Exposure of Sensitive Information | HIGH |
| CWE-209 | Error Information Disclosure | MEDIUM |
| CWE-213 | Exposure of Sensitive Information Due to Incompatible Policies | HIGH |
| CWE-215 | Insertion of Sensitive Information Into Debugging Code | MEDIUM |
| CWE-312 | Cleartext Storage of Sensitive Information | HIGH |
| CWE-359 | Exposure of Private Personal Information (PII) | HIGH |
| CWE-522 | Insufficiently Protected Credentials | CRITICAL |
| CWE-532 | Insertion of Sensitive Information into Log File | MEDIUM |
| CWE-538 | Insertion of Sensitive Information into Externally-Accessible File | HIGH |
| CWE-540 | Inclusion of Sensitive Information in Source Code | HIGH |