Api Rate Abuse in Adonisjs with Mongodb

AdonisJS, a Node.js web framework, does not enforce request-rate controls by default. When routes interact with MongoDB through Mongoose or the native MongoDB driver, an API rate abuse vulnerability can emerge if repeated requests are allowed to exhaust database connections, overload query execution, or trigger expensive aggregation pipelines without restriction.

The risk is realized when unauthenticated or weakly authenticated endpoints accept high volumes of traffic. For example, a public search endpoint that builds dynamic MongoDB queries can be targeted to perform heavy reads, cause elevated CPU usage on the database, or increase operational costs in a hosted MongoDB service. Because AdonisJS applications often rely on asynchronous handlers and connection pools, abusive requests may queue and amplify resource consumption. This maps to common attack patterns such as API resource exhaustion and denial of service, and can degrade availability for legitimate users.

In a typical AdonisJS route file, a developer might define a handler that queries MongoDB without considering rate constraints:

// start of route handler
'use strict'
const Post = use('App/Models/Post')

class SearchController {
  async index({ request }) {
    const { q, page = 1, limit = 20 } = request.get()
    const skip = (page - 1) * limit
    // No rate limiting applied before hitting MongoDB
    const results = await Post.query()
      .where('title', 'like', `%${q}%`)
      .orWhere('body', 'like', `%${q}%`)
      .limit(Number(limit))
      .skip(Number(skip))
      .fetch()
    return results
  }
}
module.exports = SearchController

If this endpoint is exposed without middleware or service-layer controls, an attacker can issue many requests with varying query parameters, causing MongoDB to execute numerous similar queries, consume I/O, and potentially trigger long-running operations. AdonisJS’s route-level middleware can be used to implement rate limiting, but the absence of such controls in this combination creates a surface for API rate abuse.

Additionally, MongoDB’s operations can be expensive when indexes are missing or misaligned. An attacker probing with varied text search patterns may force collection scans. Monitoring for unusual spikes in database operations from a single IP or token is essential to detect ongoing abuse.

Remediation combines AdonisJS middleware, application-level rate limiting, and MongoDB-side considerations such as query shape, indexing, and read concern settings. The goal is to reduce unnecessary database load while preserving functionality.

1. Add rate-limiting middleware in AdonisJS

Use AdonisJS middleware to enforce per-identifier limits before requests reach route handlers. Below is an example using a custom middleware that tracks requests in a simple in-memory store; for production, replace this with Redis or a distributed store.

// start of middleware RateLimiter.js
'use strict'
class RateLimiter {
  constructor() {
    this.store = new Map()
  }

  async handle(ctx, next) {
    const key = ctx.request.ip
    const now = Date.now()
    const windowMs = 60 * 1000 // 1 minute
    const maxRequests = 30
    if (!this.store.has(key)) {
      this.store.set(key, { count: 1, start: now })
    } else {
      const entry = this.store.get(key)
      if (now - entry.start > windowMs) {
        entry.count = 1
        entry.start = now
      } else {
        entry.count += 1
      }
      if (entry.count > maxRequests) {
        ctx.response.status(429)
        return ctx.response.send({ error: 'Too Many Requests' })
      }
    }
    await next()
  }
}
module.exports = RateLimiter

Register this middleware in start/hooks.ts or bind it to specific routes in start/routes.ts. This prevents unbounded request bursts that would otherwise hammer MongoDB.

2. Parameterized and indexed queries

Ensure MongoDB queries use indexes and avoid collection scans. Define a compound index that supports common filter and sort patterns:

// In a seeder or migration script
'use strict'
const Post = use('App/Models/Post')

class PostSeeder {
  async up() {
    await Post.createIndexes([
      { keys: { title: 'text', createdAt: -1 } },
      { keys: { tags: 1, updatedAt: -1 } }
    ])
  }
}
module.exports = PostSeeder

Then in the handler, use bounded queries with explicit projections to minimize document scanning:

// start of optimized route handler
'use strict'
const Post = use('App/Models/Post')

class SearchController {
  async index({ request }) {
    const { q, page = 1, limit = 20 } = request.get()
    const skip = (page - 1) * limit
    // Uses text index and respects limits
    const results = await Post.query()
      .with('author')
      .where(() => {
        this.where('title', 'like', `%${q}%`)
          .orWhere('body', 'like', `%${q}%`)
      })
      .limit(Number(limit))
      .skip(Number(skip))
      .orderBy('createdAt', 'desc')
      .fetch()
    return results
  }
}
module.exports = SearchController

3. MongoDB driver-level safeguards

When using the native MongoDB driver directly, set appropriate options to mitigate abusive cursor behavior and resource exhaustion:

// start of a service file using native driver
const { MongoClient } = require('mongodb')

const uri = process.env.MONGODB_URI
const client = new MongoClient(uri, {
  maxPoolSize: 10,
  serverSelectionTimeoutMS: 5000,
  socketTimeoutMS: 20000,
  maxIdleTimeMS: 30000
})

async function safeFindUserSearches(dbName, filter, page = 1, pageSize = 20) {
  await client.connect()
  const db = client.db(dbName)
  const coll = db.collection('searches')
  // Use collation and limit to avoid runaway operations
  return await coll.find(filter)
    .sort({ timestamp: -1 })
    .skip((page - 1) * pageSize)
    .limit(pageSize)
    .toArray()
}

// Usage
safeFindUserSearches('apiDB', { query: { $exists: true } })
  .then(console.log)
  .catch(console.error)
  .finally(() => client.close())

These steps reduce the likelihood that API rate abuse will translate into database contention, excessive document scans, or operational impact.

• How does middleBrick relate to this topic?

  middleBrick scans API endpoints and returns security risk scores and findings, including rate limiting and data exposure checks that highlight API rate abuse risks. With the free tier, you can run 3 scans per month to test endpoints quickly. The Pro plan adds continuous monitoring and CI/CD integration to catch regressions early.

• Can middleware alone fully prevent API rate abuse against MongoDB?

  Middleware provides important perimeter controls, but it must be combined with MongoDB-side measures such as proper indexing, query constraints, and resource limits. The combination reduces abuse risk more effectively than either layer alone.