Distributed Denial Of Service in Adonisjs with Dynamodb

AdonisJS, a Node.js web framework, often interacts with databases through service classes or controllers. When using Amazon DynamoDB as the persistence layer, certain patterns in AdonisJS code can amplify resource exhaustion risks that resemble a distributed denial of service (DDoS) impact on the application itself, even if DynamoDB itself remains available. The risk arises from unbounded queries, missing pagination, and synchronous blocking operations that consume event loop and memory under load.

DynamoDB charges and limits are centered on read/write capacity units and request size, but an AdonisJS route that performs repeated full-table scans or queries without filtering can generate excessive consumed capacity and prolonged latency. If the route does not enforce timeouts or backpressure, concurrent requests may pile up, increasing memory usage and response times for legitimate users. This is especially true when the route builds query parameters dynamically without validating or bounding the input, allowing a single call to request an unbounded result set that ties up Node.js event loop resources in AdonisJS.

Another vector specific to this stack is missing request-level rate limiting in AdonisJS when calls hit DynamoDB-intensive endpoints. Without per-user or per-IP rate limits, a client can trigger many parallel scan or query operations. Since DynamoDB has a finite throughput, high fan-out requests can lead to throttling responses that AdonisJS may attempt to retry, further increasing load. The combination of missing input validation, missing pagination, and missing concurrency controls in AdonisJS routes makes the application susceptible to self-inflected denial of service that can be triggered without needing to compromise infrastructure.

Additionally, serialization of large DynamoDB responses in AdonisJS views or JSON APIs can block the event loop if done synchronously or without streaming. If an endpoint returns thousands of items as a single JSON payload, the serialization and network transmission time grow, tying up worker processes. In a shared runtime, this can manifest as a denial of service for other incoming requests, even though DynamoDB responded successfully. Proper use of pagination, streaming responses, and asynchronous processing is required to mitigate this class of issue.

Apply bounded queries and pagination to prevent unbounded result sets that can overload the event loop. Always use Limit and ExclusiveStartKey/LastEvaluatedKey to chunk data, and enforce server-side limits in DynamoDB. Validate and sanitize all inputs used in key conditions to avoid wildcard or scan misuse.

import { DateTime } from 'luxon'
import { DynamoDBClient, QueryCommand } from '@aws-sdk/client-dynamodb'
import { unmarshall } from '@aws-sdk/util-dynamodb'

const ddb = new DynamoDBClient({ region: 'us-east-1' })

export class ReportService {
  async paginatedOrders(userId: string, limit: number = 50, lastKey?: Record) {
    if (!userId || typeof userId !== 'string') {
      throw new Error('Invalid user id')
    }
    const cmd = new QueryCommand({
      TableName: 'orders',
      KeyConditionExpression: 'userId = :uid',
      ExpressionAttributeValues: {
        ':uid': { S: userId }
      },
      Limit: Math.min(limit, 100),
      ExclusiveStartKey: lastKey || undefined
    })
    const resp = await ddb.send(cmd)
    const items = resp.Items?.map(unmarshall) || []
    return {
      items,
      lastKey: resp.LastEvaluatedKey || null
    }
  }
}

Add explicit timeouts and retries with exponential backoff in AdonisJS services to avoid hanging requests that accumulate under concurrency. Configure the AWS SDK with a socket timeout and a total timeout to ensure failing calls release resources promptly.

import { DynamoDBClient } from '@aws-sdk/client-dynamodb'

const ddb = new DynamoDBClient({
  region: 'us-east-1',
  requestHandler: {
    timeout: 5000,
    httpsAgentOptions: { keepAlive: true }
  }
})

Implement per-route rate limiting in AdonisJS to protect DynamoDB-intensive endpoints. Use a token-bucket or sliding window approach to restrict calls per user or IP, and combine it with concurrency limits to reduce the chance of overwhelming downstream capacity.

import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import { RateLimiterMemory } from 'rate-limiter-flexible'

const limiter = new RateLimiterMemory({
  points: 100, // 100 requests
  duration: 60 // per 60 seconds
})

export class OrdersController {
  public async index({ request, response }: HttpContextContract) {
    const ip = request.ip()
    try {
      await limiter.consume(ip)
    } catch (_) {
      return response.status(429).json({ error: 'Too Many Requests' })
    }
    const service = new ReportService()
    const { items, lastKey } = await service.paginatedOrders(request.qs.userId)
    return response.ok({ items, lastKey })
  }
}

Stream large responses to avoid blocking the event loop on serialization and to allow clients to process data incrementally. Use Node.js streams in AdonisJS controllers when returning large DynamoDB result sets, and ensure proper error handling to prevent resource leaks.

import { PassThrough } from 'stream'
import { DynamoDBClient, ScanCommand } from '@aws-sdk/client-dynamodb'
import { marshall } from '@aws-sdk/util-dynamodb'

const ddb = new DynamoDBClient({ region: 'us-east-1' })

export class StreamController {
  public async *scanStream() {
    let exclusiveStartKey: Record | undefined
    while (true) {
      const cmd = new ScanCommand({
        TableName: 'largeevents',
        ExclusiveStartKey: exclusiveStartKey
      })
      const resp = await ddb.send(cmd)
      for (const item of resp.Items || []) {
        yield marshall(item)
      }
      exclusiveStartKey = resp.LastEvaluatedKey || undefined
      if (!resp.LastEvaluatedKey) break
    }
  }

  public async stream({ response }: HttpContextContract) {
    const stream = new PassThrough({ objectMode: true })
    response.header('Content-Type', 'application/json')
    response.header('Transfer-Encoding', 'chunked')
    this.scanStream().then(generator => {
      (async () => {
        for await (const chunk of generator) {
          stream.write(JSON.stringify(chunk) + '\n')
        }
        stream.end()
      })()
    })
    return response.stream(stream)
  }
}

Map findings to compliance frameworks such as OWASP API Top 10 and PCI-DSS by ensuring that high-severity items like Improper Rate Limiting (A04:2023) and Security Misconfiguration (A05:2023) are tracked in your reports. middleBrick scans can surface these categories when scanning your endpoints, and the Pro plan supports continuous monitoring so changes in risk are caught before deployment.

• Does middleBrick test for DDoS-like behavior in AdonisJS + DynamoDB setups?

  middleBrick runs 12 security checks in parallel, including Rate Limiting and Input Validation. While it does not simulate high-volume traffic, it identifies missing rate limits and unbounded query patterns that can lead to resource exhaustion in this stack.

• Can the GitHub Action fail builds if DynamoDB-intensive endpoints lack pagination or rate limiting?

  Yes. With the Pro plan, you can add the GitHub Action to CI/CD pipelines and set a risk-score threshold. If scans detect issues like missing pagination or inadequate rate limiting that raise the score above your threshold, the build will fail, helping prevent insecure configurations from reaching production.