Path Traversal in Loopback with Mongodb

Path Traversal in a LoopBack application using MongoDB typically arises when user-controlled input is used to construct file system paths or to dynamically build query selectors that traverse document relationships without proper validation. In LoopBack, models and relations can inadvertently expose traversal risks if parameters such as IDs or foreign keys are reflected into filesystem operations or deep query projections.

Consider a scenario where an API endpoint accepts a filename or directory path to retrieve a stored asset. If the endpoint concatenates user input directly into a filesystem path and then uses a MongoDB GridFS bucket or a model method to fetch the associated document, an attacker can supply sequences like ../../../etc/passwd to escape the intended directory. Even when the data layer uses MongoDB, the traversal occurs at the application path layer before the database is queried, exposing backend files that should remain private.

Another common pattern is using user-supplied references to traverse model relations inadvertently. For example, a request might include an ID meant to scope a query to a tenant or organization. If the application builds a MongoDB query by injecting that ID into nested document paths without enforcing ownership or access controls, an attacker can manipulate the ID to traverse across records they should not access. This is not a MongoDB injection in the traditional sense, but a lack of path validation in the LoopBack model definitions and route handlers that allow over-permissive where clauses or projection paths.

Because middleBrick scans the unauthenticated attack surface and includes checks for Path Traversal and Property Authorization across 12 parallel security checks, it can surface these risky patterns in LoopBack endpoints that expose filesystem or logical path operations. The scan correlates findings with the OpenAPI/Swagger spec, resolving $ref definitions to identify where path parameters or dynamic model fields might lead to unsafe traversal. This helps teams see how an attacker could leverage legitimate-looking endpoints to reach sensitive data or files.

In the context of LLM security, note that Path Traversal issues are not typically surfaced through prompt injection or system prompt leakage, but they remain critical because they expose filesystem structure or logical data boundaries. middleBrick’s focus remains on detection and reporting, not remediation, so teams must apply secure coding practices to close these gaps.

To secure Path Traversal risks in LoopBack with MongoDB, validate and sanitize all user input before using it in filesystem paths or query construction. Use strict allowlists for filenames, resolve paths with path.resolve and confine operations to a designated base directory, and enforce model-level scopes to prevent unauthorized record traversal.

For file system operations, avoid direct concatenation of user input. Instead, normalize and validate the path, ensuring it remains within the intended directory:

const path = require('path');
const baseDir = '/safe/uploads';

function getSafePath(userInput) {
  const resolved = path.resolve(baseDir, userInput);
  if (!resolved.startsWith(baseDir)) {
    throw new Error('Invalid path');
  }
  return resolved;
}

When working with MongoDB documents in LoopBack, define strict model scopes and use parameterized queries to avoid unintended document traversal. For example, if you have an Asset model tied to an Organization, scope queries by organization ID rather than relying on client-supplied references alone:

// server/models/asset.json
{
  "name": "Asset",
  "base": "PersistedModel",
  "options": {
    "validateUpsert": true
  },
  "properties": {
    "name": { "type": "string" },
    "orgId": { "type": "string" }
  },
  "validations": [],
  "relations": {
    "org": {
      "type": "belongsTo",
      "model": "Organization",
      "foreignKey": "orgId"
    }
  }
}

// server/controllers/asset.controller.js
module.exports = function (Asset) {
  Asset.findByOrg = async function (orgId, userId) {
    return Asset.find({
      where: {
        orgId: orgId,
        userId: userId
      }
    });
  };
};

Additionally, configure LoopBack’s model settings to limit which properties can be returned or filtered on by default. Use the acls and propertyDefinition settings to restrict dangerous paths in queries:

// server/models/asset.json
{
  "name": "Asset",
  "base": "PersistedModel",
  "options": {
    "validateUpsert": true
  },
  "acls": [
    { "accessType": "*", "principalType": "ROLE", "principalId": "$everyone", "permission": "DENY" },
    { "accessType": "READ", "principalType": "ROLE", "principalId": "$everyone", "permission": "ALLOW", "property": "findByOrg" }
  ],
  "properties": {
    "name": { "type": "string", "required": true },
    "orgId": { "type": "string", "required": true },
    "sensitiveField": { "type": "string", "accessList": { "read": ["admin"] } }
  }
}

By combining path normalization with tightly scoped MongoDB queries and strict model-level permissions, you reduce the attack surface for Path Traversal in LoopBack applications using MongoDB. These practices align with secure coding guidance and help ensure that user input never dictates filesystem or document traversal logic.