Brute Force Attack in Django with Dynamodb

A brute force attack against a Django application using DynamoDB as the session or user store leverages predictable authentication endpoints and weak account lockout controls. In this setup, Django’s default authentication backend can be pointed at a DynamoDB table that stores user credentials or session records. If the API request rate is not constrained, an attacker can submit many username and password combinations without triggering defenses. Because DynamoDB does not enforce built-in account lockout or throttling at the database level, the application layer must implement these protections. Without explicit rate limiting tied to user or IP identity, the unauthenticated attack surface remains open long enough for exhaustive credential attempts to succeed.

Additionally, scanning such an API with middleBrick reveals findings tied to Authentication, Rate Limiting, and BOLA/IDOR checks. For example, if login endpoints do not enforce per-user or per-IP attempt limits, middleBrick assigns a lower risk score and flags missing rate controls. The scanner also checks whether the DynamoDB-backed authentication path leaks information through timing differences or verbose error messages, which can aid attackers in narrowing valid usernames. Because the scan runs in black-box mode against the unauthenticated surface, it can detect whether the Django app exposes a predictable login route that does not adequately protect against online guessing.

Compliance frameworks such as OWASP API Top 10 highlight authentication weaknesses like this under Broken Authentication. Real incidents, such as credential stuffing campaigns, demonstrate how an API with weak controls can be abused even when backed by a managed database like DynamoDB. middleBrick’s checks for Rate Limiting and Authentication help surface these gaps, providing prioritized findings and remediation guidance before an attacker compromises accounts.

To harden Django when using DynamoDB, implement explicit rate limiting at the authentication endpoint and ensure user enumeration defenses are in place. Use a token bucket or sliding window approach keyed by username and IP address, storing attempt counts in a separate DynamoDB table or using an in-memory cache with short TTLs. The following example shows a custom authentication backend that checks attempt counts before verifying credentials, leveraging the AWS SDK for Python (boto3) with DynamoDB.

import boto3
import hashlib
from django.conf import settings
from django.contrib.auth.backends import BaseBackend
from botocore.exceptions import ClientError

TABLE_NAME = 'auth_attempts'

def get_dynamodb_client():
    return boto3.client(
        'dynamodb',
        region_name=settings.AWS_REGION,
        aws_access_key_id=settings.AWS_ACCESS_KEY_ID,
        aws_secret_access_key=settings.AWS_SECRET_ACCESS_KEY,
    )

def get_attempt_key(identifier):
    return hashlib.sha256(identifier.encode('utf-8')).hexdigest()

def increment_attempt(identifier):
    client = get_dynamodb_client()
    key = get_attempt_key(identifier)
    try:
        client.update_item(
            TableName=TABLE_NAME,
            Key={'pk': {'S': f'attempt#{key}'}},
            UpdateExpression='ADD attempts :inc',
            ExpressionAttributeValues={':inc': {'N': '1'}},
            ReturnValues='UPDATED_NEW',
        )
    except ClientError:
        client.put_item(
            TableName=TABLE_NAME,
            Item={
                'pk': {'S': f'attempt#{key}'},
                'attempts': {'N': '1'},
                'last_ts': {'N': str(int(time.time()))},
            }
        )

def get_attempt_count(identifier):
    client = get_dynamodb_client()
    key = get_attempt_key(identifier)
    try:
        resp = client.get_item(TableName=TABLE_NAME, Key={'pk': {'S': f'attempt#{key}'}})
        return int(resp.get('Item', {}).get('attempts', {'N': '0'})['N'])
    except ClientError:
        return 0

class DynamoAuthBackend(BaseBackend):
    def authenticate(self, request, username=None, password=None):
        from django.contrib.auth import get_user_model
        UserModel = get_user_model()
        identifier = f'{username}:{request.META.get("REMOTE_ADDR")}'
        if get_attempt_count(identifier) >= 10:
            return None
        try:
            user = UserModel.objects.get(username=username)
            if user.check_password(password):
                increment_attempt(identifier)
                return user
        except UserModel.DoesNotExist:
            increment_attempt(identifier)
        return None

    def get_user(self, user_id):
        from django.contrib.auth import get_user_model
        return get_user_model().objects.filter(pk=user_id).first()

In this pattern, DynamoDB handles the storage of attempt counters with low-latency updates, while Django’s authentication pipeline remains the policy enforcement point. You should also configure middleware to apply rate limits on the login URL path, using the same DynamoDB backend to share state across workers. middleBrick’s scans can verify that such controls are present by checking for Rate Limiting and Authentication findings, ensuring that brute force risks are reduced. For production, combine this with secure password hashing, multi-factor options, and monitoring on anomalous sign-in patterns.