Broken Authentication in Django with Dynamodb

Broken Authentication occurs when application functions related to authentication and session management are implemented incorrectly, allowing attackers to compromise passwords, keys, or session tokens. In a Django application using Amazon DynamoDB as the user store, several factors specific to this stack can create or expose authentication weaknesses.

Django’s built-in authentication framework is designed with relational databases in mind. When developers configure Django with DynamoDB, they often replace the default relational database backend but retain Django’s ORM expectations. This mismatch can lead to subtle logic flaws. For example, if user queries do not correctly enforce uniqueness on usernames or email fields at the DynamoDB level, an attacker might create multiple accounts with the same identifier, bypassing uniqueness checks that Django assumes are enforced.

DynamoDB’s eventual consistency model can also introduce race conditions during authentication. Consider a login flow where a user updates their password or security settings. If the subsequent login query reads from a stale replica due to DynamoDB’s consistent read defaults, an attacker could use an old, compromised credential to authenticate successfully even after the legitimate user has rotated their password. This is a classic authentication bypass vector rooted in storage behavior rather than application code alone.

Another critical area is credential handling. Storing passwords requires robust hashing with salts. If the integration layer does not properly apply Django’s password hashers (e.g., PBKDF2 with SHA256) before storing the digest in DynamoDB, plaintext or weakly hashed credentials may be persisted. Additionally, session keys stored in DynamoDB must be encrypted at rest and managed with strict access controls. Misconfigured AWS Identity and Access Management (IAM) policies can allow unauthorized DynamoDB read or write access to authentication tables, enabling token or session key extraction (e.g., via an over-permissive dynamodb:GetItem policy on the authentication table).

Common attack patterns include credential stuffing using leaked credentials, token hijacking via poorly secured session stores, and privilege escalation through manipulated DynamoDB conditional writes. Real-world advisories, such as CVE-2023-23969 patterns involving insecure direct object references in NoSQL stores, illustrate how malformed queries can expose authentication endpoints. Without proper scanning, these issues remain hidden because DynamoDB does not enforce relational integrity constraints that would immediately flag anomalies.

Remediation focuses on aligning Django’s authentication expectations with DynamoDB’s operational model while enforcing strict security controls. Below are concrete, executable code examples that address storage, hashing, and access patterns.

1. Secure User Model with DynamoDB and Proper Hashing

Define a custom user model that ensures passwords are hashed using Django’s built-in hashers and stored securely in DynamoDB. Use conditional writes to enforce uniqueness on email and username.

import boto3
from django.contrib.auth.models import AbstractBaseUser, BaseUserManager
from django.contrib.auth.hashers import make_password, check_password
from django.core.exceptions import ValidationError

# Configure DynamoDB resource
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
USERS_TABLE = dynamodb.Table('auth_users')

class DynamoUserManager(BaseUserManager):
    def create_user(self, email, username, password=None, **extra_fields):
        if not email:
            raise ValidationError('Email is required')
        if not username:
            raise ValidationError('Username is required')
        hashed_password = make_password(password)
        # Enforce uniqueness via conditional write
        try:
            USERS_TABLE.put_item(
                Item={
                    'email': email,
                    'username': username,
                    'password': hashed_password,
                    'is_active': extra_fields.get('is_active', True)
                },
                ConditionExpression='attribute_not_exists(email) AND attribute_not_exists(username)'
            )
        except USERS_TABLE.meta.client.exceptions.ConditionalCheckFailedException:
            raise ValidationError('Email or username already exists')
        return {'email': email, 'username': username}

    def authenticate(self, email=None, password=None):
        response = USERS_TABLE.get_item(Key={'email': email})
        item = response.get('Item')
        if item and check_password(password, item['password']):
            return item
        return None

class DynamoUser(AbstractBaseUser):
    email = None
    username = None
    password = None
    is_active = True

    objects = DynamoUserManager()

    USERNAME_FIELD = 'email'
    REQUIRED_FIELDS = ['username']

    def has_perm(self, perm, obj=None):
        return True
    
    def has_module_perms(self, app_label):
        return True

2. Secure Session Storage in DynamoDB

Store session data with encryption and strict IAM policies. Use a dedicated table with time-based TTL and conditional writes to prevent session fixation.

import boto3
import secrets
from datetime import datetime, timedelta

SESSION_TABLE = dynamodb.Table('auth_sessions')

def create_session(user_id):
    session_key = secrets.token_urlsafe(32)
    expiry = datetime.utcnow() + timedelta(days=1)
    SESSION_TABLE.put_item(
        Item={
            'session_key': session_key,
            'user_id': user_id,
            'expires_at': expiry.isoformat(),
            'created_at': datetime.utcnow().isoformat()
        },
        ConditionExpression='attribute_not_exists(session_key)'
    )
    return session_key

def validate_session(session_key):
    response = SESSION_TABLE.get_item(Key={'session_key': session_key})
    item = response.get('Item')
    if item and datetime.fromisoformat(item['expires_at']) > datetime.utcnow():
        return item['user_id']
    return None

3. IAM and Access Control Best Practices

Apply least-privilege IAM roles. For example, the authentication lambda should have a policy scoped to specific table actions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "dynamodb:GetItem",
                "dynamodb:PutItem"
            ],
            "Resource": "arn:aws:dynamodb:us-east-1:123456789012:table/auth_users",
            "Condition": {
                "ForAllValues:StringEquals": {
                    "dynamodb:LeadingKeys": ["${cognito-identity.amazonaws.com:sub}"]
                }
            }
        }
    ]
}

Finally, integrate middleBrick scans to continuously monitor your API’s authentication surface. Use the CLI to validate changes: middlebrick scan <url>. For teams, the Pro plan enables continuous monitoring and CI/CD integration to fail builds if risk scores degrade, ensuring authentication issues are caught early.