Integrity Failures in Adonisjs with Mongodb

AdonisJS encourages rapid development with its ActiveRecord-style models and Lucid ORM, but when paired directly with raw MongoDB operations (e.g., using the official MongoDB Node.js driver or a custom Mongodb service), integrity safeguards can be bypassed. Integrity failures occur when application-level checks are incomplete or inconsistently applied, allowing an attacker to modify or substitute data in ways the developer did not intend.

In this stack, a common pattern is to use Mongodb for high-throughput or unstructured data while AdonisJS handles authentication and request lifecycle. If you construct update payloads by merging user input directly into a Mongodb update document without strict schema validation or type coercion, you risk mass assignment and type confusion. For example, an attacker can supply fields like isAdmin or role in a request body that your AdonisJS controller passes to a Mongodb $set operation, altering permissions or elevation of privileges.

Another vector arises from inconsistent object IDs. AdonisJS may use numeric or UUID identifiers, while Mongodb uses ObjectId. If your code converts or casts IDs improperly—such as using string concatenation to build queries—attackers can exploit type juggling to access or modify records outside their scope, effectively performing an Insecure Direct Object Reference (IDOR) that integrity controls should prevent.

Schema-less nature of Mongodb also amplifies the risk. Without a strongly enforced schema at the database level, unexpected or malicious fields can be written and later returned to users or passed to other services. When AdonisJS serializes query results to JSON for API responses, these unchecked fields may expose sensitive data or enable client-side logic manipulation, leading to integrity violations downstream.

Additionally, when using Mongodb change streams or event-based logic within AdonisJS, improperly validated payloads can trigger unintended side effects. If your event handlers assume certain field structures without validating against a known schema (e.g., using JSON Schema or runtime validation libraries), an attacker can inject malformed documents that cause application logic to diverge from expected behavior, undermining data integrity.

These issues map to common weaknesses in the OWASP API Security Top 10, particularly Broken Object Level Authorization (BOLA) and Mass Assignment. They are detectable in scans that compare OpenAPI/Swagger specifications with runtime behavior, highlighting mismatches between declared input constraints and actual data acceptance by the Mongodb layer.

To mitigate integrity failures when using Mongodb with AdonisJS, enforce strict validation and canonicalization on all inputs before they reach the database. Treat Mongodb update operators as sensitive constructs and never directly merge request bodies into them.

1. Use a validation layer with explicit whitelisting

Define a validation schema that explicitly permits only safe fields and applies proper coercion. Use a library compatible with AdonisJS, such as celebrate (based on Joi) or AdonisJS Validator, to sanitize inputs before constructing Mongodb update documents.

const { schema, rules } = use('celebrate');

const updateProfileSchema = schema.create({
  body: schema.object({
    email: schema.string.email(),
    username: schema.string.minLength(3).maxLength(50),
    // explicitly disallow sensitive fields
  }, { allowUnknown: false })
});

// In your controller
async updateProfile({ request, response }) {
  const payload = await validate(request, updateProfileSchema);
  const user = await UserModel.findOrFail(request.params().id);

  // Safe: only whitelisted fields are passed
  await user.merge({
    email: payload.email,
    username: payload.username
  }).save();

  return user;
}

2. Explicitly cast and validate ObjectId usage

Ensure that IDs used in Mongodb queries are properly cast to ObjectId and never derived from raw user input without verification. This prevents IDOR via type confusion.

const { ObjectId } = require('mongodb');

async function findDocumentById(idString) {
  let id;
  try {
    id = new ObjectId(idString);
  } catch (err) {
    throw new Error('Invalid ObjectId');
  }
  const doc = await db.collection('documents').findOne({ _id: id });
  if (!doc) {
    throw new Error('Not found');
  }
  return doc;
}

3. Avoid direct passthrough of update payloads

Never do this:

// UNSAFE: directly using request body in update
await db.collection('users').updateOne(
  { _id: new ObjectId(userId) },
  { $set: request.body }
);

Instead, explicitly map allowed fields:

const allowedUpdates = ['profile', 'preferences', 'theme'];
const updateObject = { $set: {} };

allowedUpdates.forEach(key => {
  if (request.body.hasOwnProperty(key)) {
    updateObject.$set[key] = request.body[key];
  }
});

await db.collection('users').updateOne(
  { _id: new ObjectId(userId) },
  updateObject
);

4. Enforce schema validation on retrieved documents

When returning data from Mongodb, validate against a schema before sending responses to ensure integrity of the data structure.

const profileSchema = schema.create({
  _id: schema.string(),
  email: schema.string.email(),
  username: schema.string.minLength(1),
  isAdmin: schema.boolean.optional()
});

const raw = await db.collection('users').findOne({ _id: new ObjectId(userId) });
const validated = validate(raw, profileSchema);
return validated;

5. Use parameterized queries for aggregation pipelines

If you use aggregation with dynamic stages, validate and parameterize each stage to prevent injection or malformed pipeline construction.

const pipeline = [
  { $match: { status: 'active' } },
  { $project: {
    id: '$_id',
    email: 1,
    // whitelist fields explicitly
  }}
];

const results = await db.collection('users').aggregate(pipeline).toArray();