Cache Poisoning in Flask with Dynamodb

Cache poisoning in a Flask application that uses DynamoDB typically occurs when responses keyed by request-derived values are stored in a cache and later reused for different users or requests. Because Flask does not enforce strict separation between request context and cache keys, an attacker can manipulate inputs that influence the cache key or the cached content, leading to one user receiving another user’s data or seeing modified responses. When DynamoDB is used as the backend data source, cached items that include sensitive user attributes—such as user IDs, tenant identifiers, or internal object keys—can be incorrectly reused if the cache key does not incorporate a strong user or context component.

Consider an endpoint that queries DynamoDB for an item using a path parameter such as item_id and caches the result. If the cache key is built only from item_id and not from the authenticated user’s identity, two different users requesting the same item_id will receive the same cached response. An attacker who can trick a victim into making a request with a crafted query parameter or authenticated session can cause the application to cache a victim’s response under a shared key. Subsequent requests by the attacker then read that poisoned cache entry, exposing the victim’s data or enabling privilege escalation if the cached response is augmented with elevated permissions.

DynamoDB-specific attributes such as partition keys and sort keys can inadvertently become part of the cache key if the query parameters are reflected directly. For example, if a Flask route uses a user-controlled user_id to form a DynamoDB query and then caches the raw JSON response, an attacker supplying a different user_id may observe that the cache key changes only by that identifier. If the caching layer does not isolate entries by session or token, the attacker can read another user’s data. In addition, if the application caches error responses or empty results for specific query patterns, an attacker can flood the cache with misleading entries, causing denial-of-view for legitimate users.

Because middleBrick tests unauthenticated attack surfaces and includes checks such as Input Validation and Property Authorization, it can surface cache poisoning risks by identifying endpoints where user-influenced data flows into caching logic without proper context isolation. The scanner flags cases where authorization checks are missing or inconsistent across cached resources, highlighting the need to bind cache entries to the requesting identity and to validate that cached data remains appropriate for each consumer.

To remediate cache poisoning in Flask with DynamoDB, ensure that cache keys incorporate a strong, immutable user or session context and that responses are validated before use. Do not rely solely on object identifiers; include the authenticated user’s ID or a per-session token in the cache key. Enforce strict authorization on every DynamoDB query so that cached results are never served outside the intended security context. The following examples demonstrate a secure pattern using Flask, the AWS SDK for Python (Boto3), and server-side caching.

First, define a helper to build a cache key that combines the endpoint name, the authenticated user identity, and the logical item identifier:

import hashlib
def make_cache_key(user_id, item_id):
    raw = f'user:{user_id}:item:{item_id}'
    return hashlib.sha256(raw.encode('utf-8')).hexdigest()

Next, enforce authorization before querying DynamoDB and ensure the user is allowed to access the item. Use Boto3 to retrieve the item, and only cache after confirming ownership or appropriate permissions:

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

dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table_name = 'Items'

def get_item(user_id, item_id):
    # Enforce authorization: ensure the item belongs to the user
    table = dynamodb.Table(table_name)
    try:
        response = table.get_item(
            Key={'user_id': user_id, 'item_id': item_id},
            ConsistentRead=True
        )
    except ClientError as e:
        raise RuntimeError(f'DynamoDB error: {e.response[\"Error\"][\"Code\"]}')

    item = response.get('Item')
    if item is None:
        raise ValueError('Item not found')
    # Ensure the item’s ownership matches the requester
    if item.get('user_id') != user_id:
        raise PermissionError('Unauthorized access')
    return item

Then integrate caching in Flask so that each cache entry is scoped to the user and item, and validate cached content before use:

from flask import Flask, jsonify, g
import hashlib

app = Flask(__name__)

@app.route('/items/<item_id>')
def show_item(item_id):
    user_id = g.current_user.id  # authenticated identity from session or token
    cache_key = make_cache_key(user_id, item_id)
    cached = cache.get(cache_key)
    if cached is not None:
        return jsonify(cached)

    item = get_item(user_id, item_id)
    cache.set(cache_key, item, timeout=300)
    return jsonify(item)

For higher assurance, include additional context such as the current user’s role or tenant ID in the cache key, and avoid caching responses that contain sensitive fields unless those fields are required and properly masked. Rotate cache keys on privilege changes and implement cache invalidation when data is updated to prevent stale or poisoned entries from being served.