Api Rate Abuse in Hanami with Mongodb

Rate abuse in a Hanami application backed by MongoDB occurs when an API endpoint does not enforce sufficient request limits, allowing a single client to generate a high volume of operations against the database. Hanami routes map to Ruby methods that typically perform MongoDB queries through an ODM like Mongoid or the official MongoDB Ruby driver. If these endpoints lack per-client throttling or global concurrency caps, an attacker can issue many rapid requests that translate into repeated queries, aggregations, or writes. This can manifest as query flooding, index exhaustion, or excessive document reads/writes, leading to elevated resource consumption and potential denial of service for legitimate users.

Because MongoDB operations are often asynchronous from the web process in Hanami, the perceived latency of each request may be low even while the database handles a large number of concurrent operations. Without rate limiting, the API surface grows as new routes are added, increasing the unauthenticated attack surface that tools like middleBrick test. For example, an endpoint that returns a list of user documents with a broad filter and no pagination can be hammered to exhaust available connections or trigger long-running scans. middleBrick’s rate limiting check would flag such an endpoint if it detects missing or insufficient controls, referencing the OWASP API Top 10 and API4:2023 (Resource Exhaustion).

A concrete scenario: a search route accepts a query parameter and performs a MongoDB find with a regular expression on a non-indexed field. Repeated calls with varying terms cause collection scans that consume CPU and I/O. Because Hanami does not enforce a default request cap, an unauthenticated attacker can probe this endpoint to infer data patterns or degrade performance. The risk is compounded when responses include verbose error messages that reveal stack traces or schema details, aiding further abuse. middleBrick’s unauthenticated scan would identify missing rate limiting and provide remediation guidance tied to real CVEs involving denial-of-service via resource exhaustion.

To mitigate rate abuse in Hanami with MongoDB, implement request-level throttling at the route or controller level, and enforce query constraints that limit document scope and execution time. Use a shared cache store such as Redis to track request counts per identifier (IP or API key) and reject or delay requests that exceed a defined threshold. Combine this with MongoDB-side protections like capped collections, index optimization, and bounded operations to reduce the impact of bursts.

Below are concrete, syntactically correct examples for a Hanami endpoint using the MongoDB Ruby driver. The first snippet shows a before state where no safeguards exist, and the second shows a hardened version with rate limiting and query constraints.

# Gemfile
gem 'redis'
gem 'hanami-router'

# config/settings.rb
settings.redis_url = ENV.fetch('REDIS_URL', 'redis://localhost:6379/0')

# app/actions/api/search.rb
require 'redis'

module Api
  module Search
    class Index
      include Hanami::Action

      RATE_LIMIT = 30          # max requests
      WINDOW     = 60          # per 60 seconds

      def initialize(redis: Redis.new(url: settings.redis_url))
        @redis = redis
      end

      def call(params)
        client_key = params.fetch('client_ip', 'anonymous')
        current = @redis.get("rate:#{client_key}")&.to_i || 0
        if current >= RATE_LIMIT
          self.status = 429
          return { error: 'rate limit exceeded' }.to_json
        end

        # Increment with expiry to avoid stale keys
        @redis.multi do
          @redis.incr("rate:#{client_key}")
          @redis.expire("rate:#{client_key}", WINDOW) unless @redis.ttl("rate:#{client_key}") > 0
        end

        # Safe MongoDB query with bounded fields and index-backed filter
        collection = Mongo::Client.new(['localhost:27017'], database: 'myapp')[:items]
        term = params.fetch('q', '')
        # Ensure an index exists on :name to avoid collection scans
        # collection.indexes.create_one({ name: 1 })
        results = collection.find(
          { name: { '$regex' => Regexp.escape(term), '$options' => 'i' } },
          projection: { name: 1, slug: 1 }
        ).limit(50).to_a

        { results: results }.to_json
      rescue Mongo::Error => e
        self.status = 500
        { error: 'database error' }.to_json
      end
    end
  end
end

In this example, the rate limiting uses Redis to track requests per client IP within a sliding window, returning HTTP 429 when the limit is exceeded. The MongoDB query uses a regex with an explicit projection and a limit, and it is recommended to create an index on the searched field to prevent collection scans that could be abused. For production, consider namespaced keys, more granular identifiers (e.g., API keys), and circuit breakers to complement the application-level controls.

Additionally, review your Hanami routes and ensure that sensitive or high-cost endpoints are not exposed in the unauthenticated attack surface when no credentials are required. middleBrick’s OpenAPI/Swagger spec analysis can help map which endpoints lack authentication and rate controls by resolving $ref definitions and cross-referencing spec definitions with runtime behavior. If you need to integrate checks into your development flow, the middleBrick CLI allows you to scan from terminal with middlebrick scan <url>, while the GitHub Action can add API security checks to your CI/CD pipeline and fail builds if risk scores drop below your chosen threshold.