Beast Attack in Adonisjs with Dynamodb

A Beast Attack (Bypassing Level Authorization) against an API built with AdonisJS and using DynamoDB as the persistence layer occurs when authorization checks are incomplete or inconsistent, allowing an attacker to act on resources they should not access. In this combination, the framework provides route and controller structure, while DynamoDB supplies the datastore; the vulnerability arises when authorization logic is delegated to the client, omitted in route handlers, or implemented at the table level rather than the item level.

DynamoDB itself does not enforce row-level permissions; it enforces table-level policies via IAM. If AdonisJS does not implement per-request ownership checks and scoped queries, a direct DynamoDB call with a user-supplied ID can be manipulated to access or modify other users’ items. For example, an endpoint like /api/users/:id/preferences that uses a raw get or update with the :id parameter passed directly into a DynamoDB Key without verifying that the :id belongs to the authenticated subject enables a Beast Attack.

Attack surface specifics:

• Unscoped queries: Using a global secondary index or querying by user-supplied key without a partition-key ownership filter (e.g., missing a condition like userId = :currentUserId) allows traversal across other users’ items.
• Inconsistent authorization: AdonisJS middleware may validate authentication but omit role or scope checks, or DynamoDB condition expressions may be absent, leading to privilege escalation when an attacker changes IDs in requests.
• Overly permissive IAM: If the Lambda/container role attached to AdonisJS grants dynamodb:GetItem and dynamodb:UpdateItem at the table level without resource-level constraints, an attacker can leverage any valid item key.

Real-world patterns include changing numeric IDs, UUIDs, or slugs in URLs or headers to reference other records. Because the API is unauthenticated in black-box scans, middleBrick would flag missing authorization checks between the route layer and the DynamoDB request as a BOLA/IDOR finding, with severity high and remediation guidance to enforce ownership at the data-access layer.

Remediation centers on enforcing ownership and scoping every DynamoDB operation with the authenticated subject’s identifier, avoiding reliance on client-supplied IDs alone, and validating input against the caller’s identity.

1. Always include the user identifier in the partition key or as a filter on the query. Do not trust route parameters for access control.

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

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

export async function getUserPreferences(userId, itemId) {
  const command = new GetItemCommand({
    TableName: process.env.USER_PREFERENCES_TABLE,
    Key: marshall({ userId, preferenceId: itemId })
  })
  const resp = await client.send(command)
  return resp.Item ? unmarshall(resp.Item) : null
}

2. Use condition expressions to ensure the item’s ownership on updates and deletes. This prevents overwriting another user’s data even if the ID is guessed or iterated.

export async function updateUserPreference(userId, itemId, update) {
  const command = new UpdateItemCommand({
    TableName: process.env.USER_PREFERENCES_TABLE,
    Key: marshall({ userId, preferenceId: itemId }),
    UpdateExpression: 'set #val = :value',
    ConditionExpression: 'userId = :uid',
    ExpressionAttributeNames: { '#val': 'value' },
    ExpressionAttributeValues: marshall({ ':value': update.value, ':uid': userId })
  })
  await client.send(command)
  return true
}

3. If using a sort key for scoping, design keys to embed the user ID (e.g., USER#123) and avoid generic keys that allow enumeration. Validate the format server-side before issuing DynamoDB calls.

export async function safeUpdate(userId, resourceType, resourceId, data) {
  const pk = `USER#${userId}`
  const sk = `${resourceType}#${resourceId}`
  const command = new UpdateItemCommand({
    TableName: process.env.MAIN_TABLE,
    Key: marshall({ pk, sk }),
    UpdateExpression: 'set #data = :d',
    ConditionExpression: 'begins_with(pk, :prefix)',
    ExpressionAttributeNames: { '#data': 'data' },
    ExpressionAttributeValues: marshall({ ':d': data, ':prefix': 'USER#' })
  })
  await client.send(command)
}

4. Apply middleware in AdonisJS that resolves the authenticated user’s ID and injects it into the service layer, rather than passing raw params to DynamoDB. Combine this with input validation (e.g., uuid/integer shape checks) to reduce injection and traversal risks.

import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'

async function requireUserId({ params, auth }: HttpContextContract) {
  const user = await auth.authenticate()
  const itemId = params.id
  if (!itemId || typeof itemId !== 'string') {
    throw new Error('Invalid itemId')
  }
  return { userId: user.id, itemId }
}

5. For scans or queries that must list resources, use a composite key design so that a query scoped to the user’s partition is required. Avoid FilterExpression as the primary access control; it is evaluated after reading data and can still incur cost and leakage if pagination is misused.

By coupling AdonisJS route/controller logic with tightly scoped DynamoDB key designs and conditional writes, Beast Attack risks are materially reduced: the API no longer trusts the caller to identify resources, and every request verifies ownership at the data-access boundary.