Insecure Deserialization in Adonisjs with Firestore

Insecure deserialization occurs when an application processes untrusted serialized data without sufficient integrity checks. In an AdonisJS application that uses Google Cloud Firestore, this typically arises when data received from HTTP requests or external sources is deserialized and then passed directly to Firestore operations. AdonisJS relies on structured objects for models and requests; if an attacker can supply a serialized payload that is later deserialized and used to construct Firestore queries or document updates, they may be able to inject unexpected operators, paths, or values.

Consider an endpoint that accepts a JSON payload intended to filter or update Firestore documents. If the server deserializes this payload and uses it directly in a where or update call without strict validation, an attacker can supply operator-like keys such as $ne, $in, or $exists that Firestore interprets as query modifiers rather than plain data. This is a classic injection via deserialization: the attacker’s crafted input changes the semantics of the Firestore operation, leading to unauthorized data access or modification.

Additionally, if the application reconstructs objects from serialized formats (e.g., Protocol Buffers, MessagePack, or custom formats) before passing them to Firestore, maliciously crafted objects can exploit type confusion or property injection. For example, an attacker could embed paths like __proto__ or constructor to affect object behavior in JavaScript runtime, which may influence how Firestore document data is processed or merged. Since Firestore rules evaluate document data and request contexts, improperly deserialized inputs can bypass intended rule constraints if the data shapes are not strictly validated before evaluation.

AdonisJS middleware and body parsers may inadvertently allow deeply nested or polymorphic structures that, when deserialized, produce objects with unexpected prototypes or methods. If these objects are then used to build Firestore document references or query constraints, the application may expose sensitive documents or allow privilege escalation. For instance, an attacker could supply a deserialized object that includes special Firestore field paths or reserved keys that alter query behavior, such as injecting a limit or orderBy via manipulated input that is not sanitized.

Another scenario involves logging or error handling: if deserialized objects are included in logs or error messages returned by Firestore, sensitive data may be exposed through verbose error traces or audit logs. This is particularly relevant when Firestore operations fail due to malformed input, and the application reflects deserialized content in responses without sanitization, inadvertently leaking internal structure or credentials.

Overall, the combination of AdonisJS’s flexible request handling and Firestore’s powerful query syntax creates a surface where insecure deserialization can lead to unauthorized data access, query manipulation, or exposure of sensitive information. Mitigation requires strict schema validation, whitelisting of allowed fields and operators, and avoiding direct use of deserialized user input in Firestore operations.

To secure AdonisJS applications using Firestore, apply strict validation and canonicalization before any Firestore interaction. Use Joi or Zod to define an explicit schema for incoming data, ensuring only expected fields and types are accepted. Reject any keys that resemble Firestore operators ($-prefixed) unless explicitly required and safely handled.

Example: validating and sanitizing input before using it in a Firestore query.

import { schema, rules } from '@ioc:Adonis/Core/Validator'
import { Firestore } from '@google-cloud/firestore'

const firestore = new Firestore()

const eventSchema = schema.create({
  filter: schema.object({
    field: schema.string.optional(),
    value: schema.string.optional(),
    // Explicitly disallow operator keys
    operator: schema.enum(['==', '!=', '>', '<', '>=', '<=']).optional(),
  }),
})

export async function searchEvents({ request, response }) {
  const payload = await request.validate({ schema: eventSchema })

  let query = firestore.collection('events')

  if (payload.filter?.field && payload.filter.value) {
    // Safe: using validated field and operator
    query = query.where(payload.filter.field, payload.filter.operator || '==', payload.filter.value)
  }

  const snapshot = await query.get()
  const results = snapshot.docs.map(doc => ({ id: doc.id, ...doc.data() }))
  return response.json(results)
}

When updating documents, avoid passing raw deserialized objects directly. Instead, extract only allowed fields and use Firestore’s explicit update syntax.

import { schema } from '@ioc:Adonis/Core/Validator'
import { Firestore } from '@google-cloud/firestore'

const firestore = new Firestore()

const updateSchema = schema.create({
  docId: schema.string(),
  updates: schema.object({
    // Whitelist fields that can be updated
    title: schema.string.optional(),
    status: schema.string.optional(),
    // Explicitly block paths like '__proto__' or 'constructor'
  }),
  stripUnknown: true,
})

export async function updateDocument({ request, response }) {
  const payload = await request.validate({ schema: updateSchema })
  const docRef = firestore.collection('items').doc(payload.docId)

  // Safe: only known fields are included
  await docRef.update(payload.updates)

  const doc = await docRef.get()
  return response.json({ id: doc.id, ...doc.data() })
}

For Firestore security rules, enforce strict validation on incoming data shapes and reject ambiguous or operator-like keys at the backend. Combine this with server-side validation in AdonisJS to create defense-in-depth. Avoid using deserialized user input to dynamically build rule expressions; instead, map validated inputs to predefined rule conditions.

In CI/CD, use the middleBrick GitHub Action to add API security checks and fail builds if insecure patterns are detected. This helps catch deserialization risks before deployment. The CLI tool (middlebrick scan ) can be integrated into scripts to validate endpoint behavior during development, while the Web Dashboard enables tracking security scores over time.