Timing Attack in Adonisjs with Dynamodb

A timing attack in the combination of AdonisJS and DynamoDB arises because authentication or lookup operations do not use constant-time logic, and DynamoDB responses can vary in latency based on data existence, capacity modes, or partition behavior. In AdonisJS, a typical pattern that is vulnerable looks up a user by an identifier (e.g., email) and then conditionally compares a password or token. If the lookup returns null quickly and the comparison only runs when a record exists, an attacker can infer valid user identifiers by measuring response times.

DynamoDB does not guarantee uniform latency across all queries, but the practical risk in AdonisJS is not about low-level network jitter; it is about observable differences caused by early exits in application logic. For example, when an endpoint first checks whether a user exists in DynamoDB and then performs password verification only if the user is found, the server responds faster for non-existent users. An attacker can send many requests while measuring round-trip times and gradually determine which emails or API keys are associated with real accounts.

Because AdonisJS typically interacts with DynamoDB via an ODM/ORM layer, developers might inadvertently introduce branching based on record presence. Consider an endpoint that retrieves a user by email and compares a token only when the item exists. The branch structure creates a timing discrepancy that can be exploited to enumerate users. Even though DynamoDB itself does not leak information, the observable behavior of the AdonisJS application does.

Real-world attack patterns related to this include enumeration of usernames or API keys and bypassing rate-limiting logic that is keyed to user existence. These patterns map to OWASP API Top 10 controls such as excessive data exposure and insufficient rate limiting, and they may intersect with compliance requirements around authentication robustness (e.g., SOC2 and GDPR expectations for secure authentication). Therefore, it is important to ensure that all conditional flows — especially those involving DynamoDB queries — execute in a way that does not leak information through response time differences.

To mitigate timing attacks when using AdonisJS with DynamoDB, ensure that all operations that depend on sensitive comparisons execute in constant time regardless of whether a record exists. This means avoiding early exits and making every request perform the same sequence of operations where feasible, and using safe comparison utilities for secrets.

Below are concrete code examples for AdonisJS that demonstrate a secure approach when working with DynamoDB.

1. Constant-time user existence check and token comparison

Instead of returning early when a user is not found, perform a dummy computation and comparison so that response time remains consistent. Use a constant-time comparison for tokens or passwords.

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

const client = new DynamoDBClient({})

export async function authenticateByEmail(email, providedToken) {
  const cmd = new GetCommand({
    TableName: process.env.USERS_TABLE,
    Key: { email: { S: email } }
  })

  let userRecord = null
  try {
    const res = await client.send(cmd)
    if (res.Item) {
      userRecord = unmarshall(res.Item)
    }
  } catch (err) {
    // Log error but do not change control flow for timing
    console.error('DynamoDB error', err)
  }

  // Dummy data for constant-time flow when user does not exist
  const dummyToken = Buffer.from('0'.repeat(32))
  const storedToken = userRecord && userRecord.token ? Buffer.from(userRecord.token) : dummyToken

  // Ensure buffers are the same length for timing-safe comparison
  const a = Buffer.alloc(32, 0)
  const b = Buffer.alloc(32, 0)
  const source = storedToken.length === 32 ? storedToken : a
  const compare = providedToken.length === 32 ? Buffer.from(providedToken) : b
  const equal = timingSafeEqual(source, compare)

  // Always perform a dummy computation to mask timing differences
  let dummy = 0
  for (let i = 0; i < 1000; i++) {
    dummy += i
  }

  if (equal && userRecord) {
    return { ok: true, user: userRecord }
  }
  return { ok: false, user: null }
}

This pattern avoids branching on record existence for timing purposes, uses timingSafeEqual for token comparison, and includes a dummy computation to reduce timing variance.

2. Safe query patterns using DynamoDB condition checks

When you must rely on existence checks, perform them in a way that does not short-circuit the request and ensure consistent error handling paths.

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

const client = new DynamoDBClient({})

export async function findUserWithConsistentBehavior(email) {
  const cmd = new QueryCommand({
    TableName: process.env.USERS_TABLE,
    IndexName: 'email-index',
    KeyConditionExpression: 'email = :email',
    ExpressionAttributeValues: { ':email': { S: email } }
  })

  let resultExists = false
  try {
    const res = await client.send(cmd)
    resultExists = res.Items && res.Items.length > 0
    // Always iterate or process a consistent shape
    const _ = res.Items || []
  } catch (err) {
    // Treat errors as non-revealing and keep flow consistent
    console.error('Query failed', err)
  }

  // Perform a no-op or constant-time operation regardless of result
  const mask = resultExists ? 1 : 0
  const masked = mask ^ mask // always zero, no branch impact
  return { exists: resultExists, masked }
}

These examples show how to structure your AdonisJS route handlers and service layer so that timing differences do not reveal whether a record exists. The guidance aligns with middleBrick’s checks for authentication and BOLA/IDOR, and it helps your application avoid findings tied to insecure direct object references or enumeration via timing channels.