Credential Stuffing in Hanami with Dynamodb

Credential stuffing is an automated attack where valid credentials obtained from prior breaches are systematically replayed against an application to hijack accounts. Hanami, a Ruby web framework, typically uses a persistence layer such as Dynamodb to store user data. When Hanami applications interact with Dynamodb without enforcing strong authentication controls, they can expose an unauthenticated or weakly authenticated attack surface.

In a Hanami service that relies on Dynamodb as the primary data store, user credentials (e.g., email and password hashes) are often retrieved by querying items using a partition key such as email. If the application does not enforce rate limiting or multi-factor authentication, an attacker can use a credential stuffing tool to submit thousands of username and password pairs against the login endpoint. Because the authentication logic queries Dynamodb directly and returns distinct responses for valid versus invalid credentials, the attacker can infer account validity based on timing differences or HTTP status codes.

The DynamoDB query patterns commonly used in Hanami can inadvertently facilitate credential stuffing. For example, a typical retrieval might use a condition that expects a single item, but without proper rate limiting on the API or the underlying endpoint, an attacker can perform high-volume requests. Additionally, if session tokens or API keys are stored alongside user data in Dynamodb and are returned or logged in error messages, attackers can gain further insight into valid sessions. The lack of request throttling and insufficient monitoring of authentication anomalies amplify the risk.

Another contributing factor is the use of unauthenticated or misconfigured endpoints in the Hanami application that expose user enumeration through timing or error messages. When combined with Dynamodb queries that return different latency for existing users, these endpoints provide a reliable signal for attackers. The absence of adaptive throttling or CAPTCHA challenges during authentication flows further weakens the defense against automated credential submission.

To identify these risks in practice, security scanning tools such as middleBrick can be used to assess the unauthenticated attack surface of Hanami applications that interface with Dynamodb. By running checks for authentication weaknesses, input validation issues, and rate limiting gaps, middleBrick can surface findings related to credential stuffing and provide prioritized remediation guidance without requiring access credentials.

Remediation focuses on reducing the effectiveness of credential stuffing by hardening authentication flows and securing Dynamodb interactions. Implement rate limiting at the API gateway or application level to restrict the number of login attempts per user or IP address. Introduce exponential backoff and require multi-factor authentication for suspicious sign-in patterns.

Ensure that queries to Dynamodb do not leak information through timing differences. Use consistent response times and avoid branching logic based on whether a user exists. When retrieving user data, perform a constant-time comparison for credentials where possible and avoid returning detailed error messages that distinguish between missing users and incorrect passwords.

Below are concrete code examples for a Hanami service that interacts with Dynamodb securely.

require 'aws-sdk-dynamodb'
require 'bcrypt'

class UserRepository
  def initialize
    @dynamodb = Aws::DynamoDB::Client.new(region: 'us-east-1')
    @table_name = 'users'
  end

  # Securely fetch user by email with constant-time behavior
  def find_by_email(email)
    resp = @dynamodb.get_item(
      table_name: @table_name,
      key: { email: { s: email } }
    )
    item = resp.item
    return nil unless item

    # Map DynamoDB types to Ruby values
    {
      email: item['email']['s'],
      password_hash: item['password_hash']['s'],
      mfa_enabled: item['mfa_enabled']['BOOL']
    }
  end

  # Verify password with BCrypt (constant-time comparison)
  def verify_password(user, input_password)
    return false unless user
    BCrypt::Password.new(user[:password_hash]) == input_password
  end
end

class AuthenticationService
  def initialize(user_repo)
    @user_repo = user_repo
    @rate_limiter = RateLimiter.new(window: 60, max_attempts: 5)
  end

  def authenticate(email, password, ip)
    return { error: 'Too many attempts' } unless @rate_limiter.allow?(ip)

    user = @user_repo.find_by_email(email)
    if @user_repo.verify_password(user, password)
      if user[:mfa_enabled]
        { status: 'mfa_required' }
      else
        { status: 'success', token: generate_session_token(user) }
      end
    else
      # Always return a generic response and consume time
      BCrypt::Password.new('$2a$12$DummyHashForTimingConsistencyOnly')
      { error: 'Invalid credentials' }
    end
  end

  private

  def generate_session_token(user)
    # Secure token generation omitted for brevity
    'token_placeholder'
  end
end

class RateLimiter
  def initialize(window:, max_attempts:)
    @window = window
    @max_attempts = max_attempts
    @store = {} # Use a production-grade store in practice
  end

  def allow?(key)
    now = Time.now.to_i
    @store[key] ||= []
    @store[key].reject! { |t| t < now - @window }
    if @store[key].size < @max_attempts
      @store[key] << now
      true
    else
      false
    end
  end
end

In addition to code changes, integrate middleBrick into your workflow to continuously monitor authentication endpoints and Dynamodb-related findings. Using the CLI tool, you can run middlebrick scan <url> to quickly evaluate risk scores and receive prioritized remediation steps. For teams with CI/CD pipelines, the GitHub Action can enforce security thresholds before deployment, while the MCP Server enables scanning directly from AI coding assistants to catch insecure patterns early.

Finally, map your findings to compliance frameworks such as OWASP API Top 10 and SOC2. Prioritize fixes for Authentication, BOLA/IDOR, and Rate Limiting checks, and consider upgrading to the Pro plan for continuous monitoring and alerting that help maintain a strong security posture against credential stuffing.