Distributed Denial Of Service in Flask with Dynamodb

A DDoS risk in a Flask service that uses DynamoDB typically arises from how the application handles request volume, query patterns, and error states rather than from DynamoDB itself being the direct attack vector. When Flask routes issue inefficient or unbounded requests to DynamoDB, resource consumption on the server side can amplify the impact of high request rates. For example, a route that performs a full table scan or repeatedly queries non-indexed attributes under high concurrency can cause prolonged execution times, tying up worker processes and connection pools. This can lead to thread exhaustion or elevated latency for legitimate traffic, effectively turning application logic into an amplification mechanism.

In a black-box scan, middleBrick tests behaviors such as missing rate limiting and missing concurrency controls. Without rate limiting, an unauthenticated attacker can send many requests that trigger repeated DynamoDB operations, consuming read/write capacity and potentially causing throttling responses that degrade availability. If the Flask app does not implement proper error handling for throttling (ProvisionedThroughputExceededException or similar), retries with backoff may be unbounded, increasing load on both the application and the database. Poorly designed queries that do not leverage partition keys efficiently can also cause hot partitions, which degrade performance for all users and can be triggered by maliciously crafted request parameters that force scans or repeated queries on a single partition.

Compliance mappings are relevant here: OWASP API Top 10 lists rate limiting and DoSS (Denial of Service) as a concern, and SOC2 availability controls expect mechanisms to protect against resource exhaustion. middleBrick checks for rate limiting and tests for behaviors that can lead to availability impact, providing findings with severity and remediation guidance rather than attempting to fix or block attacks directly.

Remediation focuses on request validation, efficient DynamoDB access patterns, and resilience to throttling. Use partition keys effectively to avoid scans, enforce server-side limits, and ensure retries are bounded and idempotent. Below are concrete patterns for a Flask app using the AWS SDK for Python (boto3).

Efficient query using partition key

Always prefer queries over scans. If you must scan, restrict the result set and use pagination.

import boto3
from flask import Flask, request, jsonify

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

@app.route('/products')
def get_products():
    partition_key = request.args.get('category')
    if not partition_key:
        return jsonify({'error': 'category is required'}), 400
    try:
        response = table.query(
            KeyConditionExpression='category = :cat',
            ExpressionAttributeValues={':cat': partition_key},
            Limit=50
        )
        return jsonify(response.get('Items', []))
    except Exception as e:
        app.logger.error('DynamoDB query error: %s', e)
        return jsonify({'error': 'internal server error'}), 500

Bounded retries and backoff for throttling

Handle ProvisionedThroughputExceededException with capped retries and incremental backoff to avoid amplifying load.

import time
import botocore
from boto3.dynamodb.conditions import Key

def query_with_backoff(table_name, key_condition, max_retries=3):
    client = boto3.client('dynamodb', region_name='us-east-1')
    for attempt in range(max_retries):
        try:
            response = client.query(TableName=table_name, KeyConditionExpression=key_condition)
            return response.get('Items', [])
        except client.exceptions.ProvisionedThroughputExceededException:
            if attempt == max_retries - 1:
                raise
            backoff = 0.1 * (2 ** attempt)
            time.sleep(backoff)
    return []

Rate limiting at the application layer

Use a lightweight in-memory or shared store to limit requests per client. This complements, but does not replace, infrastructure-level protections.

from flask import request
from collections import defaultdict
import time

request_counts = defaultdict(list)
RATE = 10  # requests
WINDOW = 60  # seconds

def is_rate_limited(client_id):
    now = time.time()
    window_start = now - WINDOW
    request_counts[client_id] = [t for t in request_counts[client_id] if t >= window_start]
    if len(request_counts[client_id]) >= RATE:
        return True
    request_counts[client_id].append(now)
    return False

@app.before_request
def enforce_rate_limit():
    client_id = request.headers.get('X-Forwarded-For', request.remote_addr)
    if is_rate_limited(client_id):
        return jsonify({'error': 'rate limit exceeded'}), 429

Input validation and avoiding expensive operations

Validate and sanitize inputs to prevent forced scans or queries on non-indexed attributes. Avoid client-side filtering that results in full scans.

@app.route('/items')
def get_items():
    sort_value = request.args.get('sort', 'id')
    # Ensure sort_value is restricted to known, indexed attributes
    allowed = {'id', 'created_at', 'name'}
    if sort_value not in allowed:
        return jsonify({'error': 'invalid sort parameter'}), 400
    # Use an indexed query; do not scan
    response = table.query(
        KeyConditionExpression='pk = :pkval',
        ExpressionAttributeValues={':pkval': 'user#' + request.args.get('user_id')},
        ScanIndexForward=(sort_value == 'name')
    )
    return jsonify(response.get('Items', []))

Use DynamoDB features appropriately

Leverage auto scaling for provisioned capacity where applicable, and design access patterns to avoid hot partitions. For serverless setups, use on-demand capacity cautiously and monitor consumed read/write capacity metrics to detect anomalous spikes that may indicate abuse.