Type Confusion in Dynamodb

Type confusion in DynamoDB occurs when applications incorrectly handle data type conversions between DynamoDB's native types and application-level types, leading to unexpected behavior or security vulnerabilities. DynamoDB uses a JSON-like data model with specific type annotations (S for string, N for number, M for map, L for list, etc.), and mismatches between expected and actual types can be exploited.

A common scenario involves numeric comparisons. DynamoDB stores numbers as strings internally, but applications often treat them as native numbers. Consider this vulnerable pattern:

const params = {
  TableName: 'Users',
  KeyConditionExpression: 'age = :age',
  ExpressionAttributeValues: {
    ':age': 25 // number type
  }
};

If an attacker provides a string like '25 OR 1=1', DynamoDB's type system might coerce this unexpectedly. The application expects a number but receives a string, leading to query manipulation.

Another manifestation involves attribute type confusion in update operations:

const params = {
  TableName: 'Products',
  Key: { id: '123' },
  UpdateExpression: 'set price = :price',
  ExpressionAttributeValues: {
    ':price': req.body.price // could be string, number, or object
  }
};

If the application expects a number but receives an object or array, DynamoDB might store it as a different type than intended, breaking downstream logic that assumes a specific type.

Map and list type confusion is particularly dangerous in DynamoDB:

const params = {
  TableName: 'Users',
  Item: {
    id: { S: 'user123' },
    permissions: req.body.permissions // expected M type, but could be L, S, or N
  }
};

An attacker could supply a string instead of a map, causing the application to misinterpret permissions data when reading it back from DynamoDB.

Detecting type confusion in DynamoDB requires examining both the data model and access patterns. middleBrick's DynamoDB scanning module specifically targets these vulnerabilities by analyzing type annotations and query patterns.

middleBrick performs type consistency analysis across your DynamoDB endpoints:

middlebrick scan https://api.example.com/dynamodb/scan

The scanner checks for:

• Missing or inconsistent type annotations in request payloads
• Unsafe type coercions in query parameters
• Dynamic type handling in update expressions
• Cross-type comparison operations

For example, middleBrick detects when numeric comparisons are performed without proper type validation:

{
  "finding": "Unsafe Numeric Comparison",
  "severity": "High",
  "location": "GET /api/users",
  "description": "Age parameter accepts string input without type validation",
  "remediation": "Validate numeric input types before DynamoDB queries"
}

The scanner also analyzes OpenAPI specifications to identify type mismatches between documented schemas and actual runtime behavior:

paths:
  /api/users:
    get:
      parameters:
        - name: age
          in: query
          schema:
            type: integer  # Documented as integer
      # middleBrick detects if endpoint accepts strings without validation

middleBrick's continuous monitoring (Pro plan) can alert you when type patterns change unexpectedly, potentially indicating exploitation attempts.

Remediating type confusion in DynamoDB requires strict type validation and proper use of DynamoDB's type system. Here are specific fixes for common vulnerabilities:

Input Validation:

function validateNumberInput(value, fieldName) {
  if (typeof value !== 'number' || isNaN(value)) {
    throw new Error(`${fieldName} must be a valid number`);
  }
  return value;
}

// Secure usage:
const params = {
  TableName: 'Users',
  KeyConditionExpression: 'age = :age',
  ExpressionAttributeValues: {
    ':age': validateNumberInput(req.query.age, 'age')
  }
};

Type-Safe Update Operations:

function validateMapInput(value, fieldName, expectedKeys) {
  if (typeof value !== 'object' || value === null || Array.isArray(value)) {
    throw new Error(`${fieldName} must be an object`);
  }
  const unexpectedKeys = Object.keys(value).filter(k => !expectedKeys.includes(k));
  if (unexpectedKeys.length > 0) {
    throw new Error(`${fieldName} contains unexpected keys: ${unexpectedKeys.join(', ')}`);
  }
  return value;
}

// Secure permissions update:
const params = {
  TableName: 'Users',
  Key: { id: { S: userId } },
  UpdateExpression: 'set permissions = :permissions',
  ExpressionAttributeValues: {
    ':permissions': validateMapInput(req.body.permissions, 'permissions', ['read', 'write', 'admin'])
  }
};

Using DynamoDB Type Wrappers:

const { DynamoDB } = require('@aws-sdk/client-dynamodb');
const dynamodb = new DynamoDB({ region: 'us-east-1' });

// Always use explicit type constructors:
function createSafeItem(id, age, permissions) {
  return dynamodb.convertJsonToAttributeValue({
    id: { S: id },
    age: { N: String(age) },
    permissions: { M: permissions }
  });
}

// This prevents type confusion by enforcing the correct structure:
const item = createSafeItem('user123', 25, { read: true, write: false });

Schema Validation with middleBrick:

middleBrick's spec analysis can validate your DynamoDB schemas against runtime behavior. The CLI can be integrated into your CI/CD pipeline:

jobs:
  security-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Scan DynamoDB APIs
        run: |
          npm install -g middlebrick
          middlebrick scan https://staging.example.com/api --fail-below B
        continue-on-error: false

This ensures type confusion vulnerabilities are caught before deployment.