Auth Bypass in Loopback with Dynamodb

Auth bypass in a Loopback application that uses DynamoDB typically occurs when authorization checks are missing or incorrectly applied at the data-access layer. Loopback models often map directly to DynamoDB tables via connectors or custom repositories. If a controller or repository method does not validate the requesting user’s permissions against the resource’s ownership or tenant context, an attacker can manipulate identifiers (e.g., changing a record ID in the URL or request body) to access or modify data they should not see.

DynamoDB’s key-based data model (partition key and sort key) means that predictable or sequential IDs are common. When authorization is only enforced at an API-gateway or coarse application boundary, but not re-validated against DynamoDB key attributes, a BOLA/IDOR condition arises. For example, a GET /users/{id} endpoint might forward {id} directly into a DynamoDB GetItem request without confirming that the authenticated user’s user_id matches the requested id. Because DynamoDB does not enforce row-level ownership natively, this results in an unauthenticated or elevated-path read of another user’s record.

In a Loopback + DynamoDB setup, additional risk arises from misconfigured IAM policies used by the connector. If the Lambda or EC2 instance role used by Loopback is overly permissive (e.g., dynamodb:GetItem on all items in a table), a compromised application component or an authenticated user can scan across partition keys using crafted requests, effectively performing a privilege escalation or data enumeration. The combination of predictable keys, missing ownership checks, and permissive IAM creates a classic Auth Bypass/IDOR vector.

Consider a scenario where the DynamoDB table uses user_id as the partition key and a timestamp as the sort key. A vulnerable endpoint might accept an item_id and call dynamodb.get with that key alone, without ensuring the item’s user_id matches the requester’s user_id. An attacker can iterate item_ids within their own partition or, if they guess another user’s partition key, read or delete that data. This pattern maps directly to OWASP API Top 10:2023 Broken Object Level Authorization and can be surfaced by middleBrick’s BOLA/IDOR check, which tests unauthenticated and authenticated traversal to detect such authorization gaps.

Real-world exploitation chains often include SSRF or Unsafe Consumption issues that further amplify risk: an attacker who can coerce the server into making AWS SDK calls to DynamoDB might attempt metadata service access or use crafted input to trigger costly queries. middleBrick’s SSRF and Unsafe Consumption checks help surface these secondary paths in a Loopback + DynamoDB deployment, complementing the Auth Bypass findings.

Remediation centers on enforcing ownership checks before executing DynamoDB operations and tightening IAM policies used by the Loopback connector. Always validate that the authenticated subject has rights to the specific key attributes in DynamoDB, and avoid using raw IDs supplied by the client as the sole lookup key.

Example: secure user profile endpoint using the AWS SDK for JavaScript v3 within a Loopback repository or controller. This ensures the requesting user can only access items where the partition key matches their user_id.

import { DynamoDBClient, GetItemCommand } from "@aws-sdk/client-dynamodb";
import { marshall } from "@aws-sdk/util-dynamodb";

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

async function getUserProfile(req, userRepository) {
  const userId = req.accessToken.userId; // authenticated subject from Loopback context
  const requestedId = req.params.id;

  if (userId !== requestedId) {
    const err = new Error('Authorization failed');
    err.statusCode = 403;
    throw err;
  }

  const params = {
    TableName: process.env.PROFILES_TABLE,
    Key: marshall({ user_id: userId })
  };

  const command = new GetItemCommand(params);
  const result = await client.send(command);
  return result.Item ? unmarshall(result.Item) : null;
}

In this snippet, the critical check if (userId !== requestedId) enforces ownership before any DynamoDB call. The partition key used in marshall({ user_id: userId }) aligns with the table design, ensuring the request targets the correct row. This pattern prevents IDOR-style access across partition keys and addresses the BOLA/IDOR category flagged by middleBrick.

For broader protection, define a Loopback repository method that encapsulates the authorization logic and key construction. This keeps DynamoDB-specific checks close to the data access layer and prevents controllers from accidentally omitting ownership validation.

class ProfileRepository {
  constructor(datasource) {
    this.datasource = datasource; // configured AWS SDK or client wrapper
  }

  async findByIdForUser(userId, id) {
    if (userId !== id) {
      const err = new Error('Not found');
      err.statusCode = 404;
      throw err;
    }
    const params = {
      TableName: process.env.PROFILES_TABLE,
      Key: marshall({ user_id: userId })
   ;
    const { Item } = await this.datasource.send(new GetItemCommand(params));
    return Item ? unmarshall(Item) : null;
  }
}

Additionally, review and scope the IAM role used by the Loopback DynamoDB connector. Apply least-privilege policies that restrict actions to items where the partition key matches the authenticated user’s context where possible, and enable CloudTrail logging for auditability. middleBrick’s IAM/permission analysis (when paired with runtime findings) can highlight overprivileged roles that enable privilege escalation.

Finally, use input validation and type checks on IDs to avoid type confusion or injection-style issues when composing DynamoDB keys. Combine these technical fixes with continuous scanning via middleBrick’s Pro plan to detect regressions; the dashboard and GitHub Action integrations can fail CI/CD when new Auth Bypass findings appear, ensuring ongoing alignment with OWASP API Top 10 and compliance mappings.