Injection Flaws in Loopback with Mongodb

Loopback is a popular Node.js framework for building APIs, and it often integrates with MongoDB for data storage. Injection flaws occur when user-controlled input is passed to database queries without proper validation or sanitization. In Loopback with MongoDB, this typically manifests when developer code constructs query objects using raw request parameters (e.g., req.query.where or dynamic filter objects) and passes them directly to the MongoDB connector.

For example, consider a Loopback model User exposed at an endpoint like /api/users. A seemingly harmless implementation might allow filtering by role via query parameters:

// Loopback model JSON configuration or remote method
{
  "accepts": [
    {
      "arg": "filter",
      "type": "object",
      "required": false,
      "http": { "source": "query" }
    }
  ],
  "returns": { "arg": "data", "type": "array", "root": true }
}

On the server side, if the handler directly uses the filter to build a MongoDB query:

User.find(filter, (err, users) => {
  if (err) return res.serverError(err);
  res.json(users);
});

An attacker can supply a malicious filter such as { role: { $ne: null } } or embed nested operators like { $where: "return this.password == 'admin'" }. Because Loopback’s model connectors translate JavaScript objects into MongoDB queries, these operators execute within the database context. This enables unauthorized data access (e.g., listing all users), privilege escalation, or data exfiltration. The unauthenticated attack surface of middleBrick testing would flag this as an Injection flaw under BOLA/IDOR and Input Validation checks, noting that attacker-supplied operators bypass intended access controls.

Additionally, if the Loopback application exposes a model over REST with strong-parameter configurations that are too permissive, an attacker can chain operators such as $regex with patterns that cause denial-of-service through catastrophic backtracking, or use $where to execute arbitrary JavaScript on the server side. These patterns are consistent with the OWASP API Top 10 A03:2023 Injection, and they map to real-world attack techniques seen in the wild. middleBrick’s runtime checks compare the effective query execution against the OpenAPI spec definitions to detect mismatches between declared and actual behavior, highlighting where developer intent diverges from runtime outcomes.

To remediate injection flaws in Loopback with MongoDB, validate and sanitize all inputs that form query objects. Prefer explicit, schema-driven filter builders instead of passing raw request objects directly to model methods. Use Loopback’s built-in where resolver with strict property whitelisting.

Secure approach using a whitelist of allowed fields:

const ALLOWED_FILTER_FIELDS = ['id', 'email', 'status'];

function sanitizeFilter(input) {
  if (!input || typeof input !== 'object') return {};
  const clean = {};
  for (const key of ALLOWED_FILTER_FIELDS) {
    if (Object.prototype.hasOwnProperty.call(input, key)) {
      // Basic type check; extend for nested fields if needed
      if (typeof input[key] === 'string' || typeof input[key] === 'number' || typeof input[key] === 'boolean') {
        clean[key] = input[key];
      }
    }
  }
  return clean;
}

// In your Loopback controller or remote method
User.find({ where: sanitizeFilter(req.query.filter) }, (err, users) => {
  if (err) return res.serverError(err);
  res.json(users);
});

This ensures only expected fields are used in the MongoDB query, preventing operator injection via fields like $where or $regex. For more complex queries, explicitly construct the filter using Loopback’s repository API:

const userRepository = User.getDataSource().repository('User');
const filters = {
  where: {
    email: req.query.email,
    // No dynamic operator injection
  }
};
userRepository.find(filters)
  .then(users => res.json(users))
  .catch(err => res.serverError(err));

Additionally, enforce strict model ACLs and role-based access controls within Loopback’s model.json or via the common/models/user.json ACL entries to ensure that even if an injection vector exists, the database operation is further constrained by authorization rules. Combine these measures with middleBrick’s continuous monitoring (available in the Pro plan) to detect regressions in your API’s security posture over time, and leverage the GitHub Action to fail builds if submitted endpoints produce risky findings.