Type Confusion in Adonisjs with Dynamodb

Type confusion in AdonisJS when interacting with DynamoDB typically arises from JavaScript’s dynamic typing combined with how data is deserialized from DynamoDB’s low-level attribute-value representation. DynamoDB stores data as typed attribute-value pairs (e.g., S for string, N for number, BOOL for boolean), and developers must explicitly convert these into JavaScript types. If the conversion logic assumes a type without validating the actual attribute type, an attacker can supply a payload that causes the runtime to interpret a value as the wrong type, leading to unexpected behavior or bypasses.

For example, an authorization check might read a user’s role from a DynamoDB item and expect a string. If the item stores the role as a number (e.g., { role: { N: '2' } }) and the code does not guard against numeric strings or numeric types, a role comparison like role === 'admin' may behave inconsistently depending on coercion rules. In AdonisJS, this can surface when using the Lucid ORM with a DynamoDB connection or when manually processing DynamoDB responses. An attacker may exploit this by tampering with input or leveraging an unvalidated response to escalate privileges or bypass access controls, aligning with BOLA/IDOR and BFLA/Privilege Escalation checks that middleBrick runs in parallel.

Consider a route that loads a configuration record from DynamoDB and uses a field to determine feature eligibility. If the field is stored as a DynamoDB string (S) but the application treats it as a boolean through loose equality, an attacker who can manipulate the stored value or the runtime representation may trigger features they should not access. This intersects with Input Validation and Property Authorization checks, where missing type guards allow invalid data to affect control flow. The risk is compounded when the same data structure is reused across contexts (e.g., serialization to JSON for an LLM endpoint), increasing the chance of unsafe consumption or output exposure, which middleBrick’s LLM/AI Security probes can surface.

In practice, type confusion in this stack does not directly leak secrets like API keys, but it can lead to authorization flaws, incorrect routing, or unsafe handling of data that middleBrick flags under Data Exposure and Unsafe Consumption checks. Because DynamoDB does not enforce a rigid schema, developers must explicitly validate attribute types before use. middleBrick’s OpenAPI/Swagger analysis (2.0, 3.0, 3.1) with full $ref resolution helps correlate runtime findings with spec definitions, making it easier to locate endpoints where type assumptions are risky.

To prevent type confusion in AdonisJS when working with DynamoDB, always validate and explicitly convert attribute types before use. Rely on strong runtime checks instead of loose equality, and normalize data as early as possible in the request lifecycle.

1. Validate and convert DynamoDB attribute types explicitly

DynamoDB returns values wrapped in type descriptors. Create helper functions to extract and validate types, ensuring the expected JavaScript type matches the actual DynamoDB type before proceeding.

import { DynamoDB } from '@aws-sdk/client-dynamodb';

const ddb = new DynamoDB({});

function getStringAttribute(item, key) {
  const attr = item[key];
  if (!attr || attr.S === undefined) {
    throw new Error(`Missing or invalid string attribute: ${key}`);
  }
  return attr.S;
}

function getNumberAttribute(item, key) {
  const attr = item[key];
  if (!attr || attr.N === undefined) {
    throw new Error(`Missing or invalid number attribute: ${key}`);
  }
  const num = Number(attr.N);
  if (Number.isNaN(num)) {
    throw new Error(`Invalid number value for attribute: ${key}`);
  }
  return num;
}

function getBooleanAttribute(item, key) {
  const attr = item[key];
  if (!attr || attr.BOOL === undefined) {
    throw new Error(`Missing or invalid boolean attribute: ${key}`);
  }
  return attr.BOOL;
}

2. Use strict equality and type-aware comparisons

After extracting values, compare them using strict equality and avoid coercive operations. When checking roles or statuses, compare against known string constants and ensure the extracted type matches expectations.

const role = getStringAttribute(dynamoItem, 'role');
if (role !== 'admin' && role !== 'editor') {
  throw new Error('Unauthorized role');
}

3. Normalize data after retrieval

When loading items for business logic, map DynamoDB responses to plain JavaScript objects with enforced types. This reduces the risk of accidentally using a raw DynamoDB item downstream, which could lead to type confusion in other modules or when exposing data to an LLM endpoint.

function normalizeUserItem(item) {
  return {
    id: getStringAttribute(item, 'id'),
    role: getStringAttribute(item, 'role'),
    level: getNumberAttribute(item, 'level'),
    active: getBooleanAttribute(item, 'active'),
  };
}

// Usage after fetching from DynamoDB
const user = normalizeUserItem(rawDdbItem);
if (user.role === 'admin') {
  // safe to proceed
}

4. Schema validation at the edge

Even when using DynamoDB’s flexible schema, enforce shape validation in AdonisJS routes or model hooks. Combine runtime checks with a validation schema (e.g., using Joi or Yup) to reject malformed or type-mismatched payloads before they reach business logic.

import Joi from 'joi';

const userSchema = Joi.object({
  id: Joi.string().required(),
  role: Joi.string().valid('admin', 'editor', 'viewer').required(),
  level: Joi.number().integer().min(0).required(),
  active: Joi.boolean().required(),
});

// Validate normalized data or incoming payloads
const { error, value } = userSchema.validate(normalizeUserItem(rawDdbItem));
if (error) {
  throw new Error(`Invalid data: ${error.message}`);
}

5. Secure serialization if exposing data to LLM endpoints

If you forward DynamoDB-derived data to an LLM endpoint, ensure serialized JSON does not mix types in a way that confuses downstream consumers. Explicitly serialize with deterministic types and avoid including type-descriptor fields (e.g., { S: '...' }) in outbound payloads.

const payload = JSON.stringify({
  id: user.id,
  role: user.role,
  level: user.level,
});
// Send `payload` to an LLM endpoint; ensure no raw DynamoDB wrappers are included

These steps reduce the attack surface related to type confusion, support findings from middleBrick’s checks such as Input Validation, Property Authorization, and Unsafe Consumption, and align with remediation guidance mapped to frameworks like OWASP API Top 10.