Insecure Deserialization in Adonisjs with Dynamodb

Insecure deserialization occurs when an application processes untrusted data to reconstruct objects without sufficient validation. In an AdonisJS application that uses DynamoDB as a persistence layer, this typically arises through two common patterns: storing serialized objects as item attributes and using custom formatters that later deserialize those attributes, and passing user-controlled payloads into DynamoDB query parameters that are later interpreted by application code.

AdonisJS does not enforce a strict serialization boundary between the framework layer and the data layer. When a developer stores a JSON-serialized representation of a class instance (for example, using JSON.stringify or a custom pickler) as a DynamoDB attribute, and later reconstructs that instance via JSON.parse or a custom unpickler, the application may execute unintended logic if the payload is manipulated. DynamoDB itself stores data as native DynamoDB types (strings, numbers, binary, sets, maps, and lists), so an application is responsible for translating those types into JavaScript objects. If the deserialization logic trusts the content of a DynamoDB item, an attacker can supply crafted values that change behavior when deserialized, leading to prototype pollution, remote code execution via gadget chains, or authentication bypass.

Because DynamoDB is schemaless at the item level, an attacker can inject unexpected attribute names or nested structures that the deserializer processes. For example, an attacker might add a key like __proto__, constructor, or other special properties that AdonisJS or underlying libraries inadvertently apply when reconstructing objects. In addition, if the application uses recursive or deep merge utilities to combine DynamoDB results with request data, those utilities may be reachable through gadget chains present in dependencies, enabling property injection or function execution. The risk is compounded when the application uses unauthenticated endpoints that expose a DynamoDB query or scan, because an attacker can probe attribute names and types to refine injection payloads without authentication.

To detect these issues, middleBrick runs checks for unsafe consumption patterns and input validation weaknesses in tandem with the API surface that interacts with DynamoDB. It evaluates whether deserialization routines validate type boundaries, sanitize special object properties, and avoid direct reconstruction of user-controlled data. The LLM/AI Security checks also probe for prompt injection and output risks that could occur if deserialized objects are used to construct prompts or responses, especially in endpoints that expose AI-related functionality.

Remediation focuses on avoiding deserialization of untrusted data and strictly validating and typing all DynamoDB attribute values before they are interpreted by application logic. Below are concrete, AdonisJS-oriented practices and code examples to reduce risk.

1. Prefer primitive types and explicit schemas

Store data in DynamoDB using primitive attribute types (strings, numbers, booleans, binary, sets) and maintain an explicit schema in your AdonisJS models or resource layers. Do not store serialized class instances. Instead, map DynamoDB items to AdonisJS models via a factory or serializer that enforces types.

// models/User.js
'use strict'
const LucidModel = use('Model')

class User extends LucidModel {
  static serializeForStorage(attributes) {
    return {
      id: attributes.id,                 // string
      email: attributes.email,           // string
      role: attributes.role,             // string
      isActive: attributes.isActive,     // boolean
      settings: JSON.stringify(attributes.settings || {}) // stringified JSON, validated downstream
    }
  }

  static deserializeFromStorage(item) {
    return {
      id: item.id,
      email: item.email,
      role: item.role,
      isActive: item.isActive,
      settings: typeof item.settings === 'string' ? JSON.parse(item.settings) : {}
    }
  }
}

module.exports = User

2. Validate and sanitize before deserialization

Always validate the shape and types of DynamoDB items before parsing JSON or reconstructing objects. Use a validation library or AdonisJS schema to enforce allowed keys and value types, and reject unexpected properties such as __proto__ or constructor.

const Joi = use('Joi')

const settingsSchema = Joi.object({
  theme: Joi.string().valid('light', 'dark').default('light'),
  notifications: Joi.boolean().default(true)
}).unknown(false) // reject extra keys

function safeDeserializeSettings(raw) {
  if (typeof raw !== 'string') {
    throw new Error('settings must be a string')
  }
  const parsed = JSON.parse(raw)
  const { error, value } = settingsSchema.validate(parsed)
  if (error) {
    throw new Error('invalid settings: ' + error.message)
  }
  return value
}

// Usage in a controller or model method
const item = await DynamoDB.get({ TableName: 'users', Key: { id: 'u-123' } }).promise()
const settings = safeDeserializeSettings(item.settings)

3. Avoid dynamic code execution and gadget chains

Do not use eval, Function, or any deserialization mechanism that can execute code. If you must store complex structures, use JSON with strict validation. Ensure dependencies are up to date and audit for known gadget chains (e.g., lodash, moment) that could be abused through prototype pollution via DynamoDB-stored data.

// Avoid this pattern entirely
const dangerous = eval('(' + rawFromDynamoDB + ')')

// Prefer this
const safe = JSON.parse(rawFromDynamoDB)
if (typeof safe !== 'object' || safe === null) {
  throw new Error('expected a plain object')
}
// Further validate with schema as shown above

4. Least-privilege IAM and parameter handling

Configure DynamoDB IAM policies to limit the operations and attributes your application can access. In AdonisJS, use environment-based configuration and avoid passing raw user input into DynamoDB expression attribute values. Use placeholders and explicitly typed values to prevent injection through attribute names or values.

const { DynamoDBClient, GetItemCommand } = require('@aws-sdk/client-dynamodb')
const client = new DynamoDBClient({ region: process.env.AWS_REGION })

async function getUserById(userId) {
  const cmd = new GetItemCommand({
    TableName: process.env.DYNAMODB_TABLE,
    Key: {
      id: { S: userId } // enforce type, do not concatenate strings
    },
    // Limit projected attributes to reduce exposure
    ProjectionExpression: 'id, email, role, isActive, settings'
  })
  const { Item } = await client.send(cmd)
  if (!Item) return null
  return {
    id: Item.id.S,
    email: Item.email.S,
    role: Item.role.S,
    isActive: Item.isActive.BOOL,
    settings: Item.settings ? JSON.parse(Buffer.from(Item.settings.B).toString('utf-8')) : {}
  }
}

5. Secure unauthenticated endpoints

If an endpoint that interacts with DynamoDB is unauthenticated, apply stricter validation and rate limiting. Do not allow filtering or sorting by arbitrary attributes that could expose sensitive data or enable injection. middleBrick’s checks for unauthenticated LLM endpoints and input validation can help surface risky patterns in API design.