Use After Free in Dynamodb

Use After Free vulnerabilities in DynamoDB typically occur when application code references database resources (tables, items, or connections) that have been deleted or released, but the application logic continues to use those references. In DynamoDB contexts, this often manifests through race conditions between delete operations and subsequent read/write attempts.

A common DynamoDB-specific pattern involves conditional writes with stale object references. Consider this scenario:

const dynamodb = new AWS.DynamoDB.DocumentClient();
const params = {
  TableName: 'users',
  Key: { id: 'user123' },
  ConditionExpression: 'attribute_exists(id)',
  UpdateExpression: 'set lastLogin = :now',
  ExpressionAttributeValues: { ':now': new Date().toISOString() }
};

// Race condition window
// Item could be deleted between these operations
await dynamodb.update(params).promise();

If another process deletes the item between the conditional check and the update operation, the application might still attempt to use the deleted item's metadata, leading to undefined behavior or security issues.

Another DynamoDB-specific manifestation occurs with global secondary indexes (GSIs). When an application references a GSI that's being modified or dropped, concurrent operations can lead to use-after-free scenarios:

const params = {
  TableName: 'orders',
  IndexName: 'customer-index', // Being modified/dropped
  KeyConditionExpression: 'customerId = :id',
  ExpressionAttributeValues: { ':id': customerId }
};

// If index is dropped during this operation
const results = await dynamodb.query(params).promise();

Batch operations present additional risks. When processing batches of DynamoDB items, if one item is deleted mid-operation, subsequent references to that item's properties can cause use-after-free vulnerabilities:

const batch = await dynamodb.batchGet({
  RequestItems: {
    'products': {
      Keys: productKeys
    }
  }
}).promise();

// Some items may have been deleted during batch operation
batch.Responses.products.forEach(product => {
  // product might be undefined or partially deleted
  processProduct(product.id, product.metadata);
});

Stream processing introduces another attack vector. When consuming DynamoDB streams, if the processing logic references items that were deleted during stream processing, use-after-free vulnerabilities can occur:

dynamodbstreams.getRecords(params, (err, data) => {
  if (err) return;
  
  data.Records.forEach(record => {
    const oldImage = record.dynamodb.OldImage;
    // OldImage might reference deleted attributes
    validateAccess(oldImage.userId);
  });
});

Detecting use-after-free vulnerabilities in DynamoDB requires both static code analysis and runtime monitoring. middleBrick's DynamoDB-specific scanning includes several detection mechanisms tailored to the service's unique characteristics.

The scanner examines your API endpoints for patterns like concurrent delete/update operations without proper synchronization. It specifically looks for:

• Conditional writes that don't handle delete race conditions
• Batch operations without error handling for missing items
• Stream processing that assumes item persistence
• Index references during modification operations

middleBrick's DynamoDB detection also analyzes your OpenAPI specifications for problematic patterns. When it finds endpoints that perform delete operations followed by operations on the same resource identifiers, it flags potential use-after-free scenarios:

$ middlebrick scan https://api.example.com/users

=== DynamoDB Security Analysis ===
• Risk Score: C (73/100)
• Critical Finding: Potential Use-After-Free in /users/{id}/update
• Category: BOLA/IDOR
• Recommendation: Implement proper delete confirmation and retry logic

The scanner's LLM/AI security module specifically tests for prompt injection vulnerabilities in DynamoDB-related AI applications, which can lead to use-after-free scenarios when AI models are fed malicious inputs that manipulate database operations.

For runtime detection, middleBrick's continuous monitoring (Pro plan) can track DynamoDB API usage patterns and alert when suspicious sequences occur, such as rapid delete-then-access operations on the same item IDs.

Remediating use-after-free vulnerabilities in DynamoDB requires implementing proper synchronization and error handling patterns. Here are DynamoDB-specific fixes for the common scenarios:

For conditional writes with race condition protection:

async function safeUpdate(userId, updateData) {
  const dynamodb = new AWS.DynamoDB.DocumentClient();
  
  // Retry logic with exponential backoff
  for (let attempt = 0; attempt < 3; attempt++) {
    try {
      const params = {
        TableName: 'users',
        Key: { id: userId },
        ConditionExpression: 'attribute_exists(id)',
        UpdateExpression: 'set lastLogin = :now, #data = :data',
        ExpressionAttributeNames: { '#data': 'data' },
        ExpressionAttributeValues: {
          ':now': new Date().toISOString(),
          ':data': updateData
        }
      };
      
      await dynamodb.update(params).promise();
      return true;
    } catch (err) {
      if (err.code === 'ConditionalCheckFailedException') {
        // Item was deleted, handle gracefully
        console.warn(`Item ${userId} no longer exists`);
        return false;
      }
      if (attempt < 2) {
        await new Promise(resolve => setTimeout(resolve, 100 * Math.pow(2, attempt)));
        continue;
      }
      throw err;
    }
  }
}

For batch operations with missing item handling:

async function safeBatchGet(keys) {
  const dynamodb = new AWS.DynamoDB.DocumentClient();
  
  try {
    const params = {
      RequestItems: {
        'products': {
          Keys: keys.map(key => ({ id: key }))
        }
      }
    };
    
    const result = await dynamodb.batchGet(params).promise();
    
    // Filter out missing items
    return result.Responses.products.filter(Boolean);
  } catch (err) {
    console.error('Batch operation failed:', err);
    return [];
  }
}

For stream processing with deleted item protection:

async function processStreamRecord(record) {
  const dynamodb = new AWS.DynamoDB.DocumentClient();
  
  if (record.eventName === 'REMOVE') {
    // Handle deleted items appropriately
    return;
  }
  
  try {
    const item = record.dynamodb.NewImage;
    
    // Verify item still exists before processing
    const params = {
      TableName: 'products',
      Key: { id: item.id.S }
    };
    
    const existing = await dynamodb.get(params).promise();
    if (!existing.Item) {
      console.warn(`Item ${item.id.S} was deleted during processing`);
      return;
    }
    
    processProduct(existing.Item);
  } catch (err) {
    console.error('Stream processing error:', err);
  }
}

For GSI-safe operations:

async function queryWithGsiFallback(customerId) {
  const dynamodb = new AWS.DynamoDB.DocumentClient();
  
  try {
    // First try with GSI
    const gsiParams = {
      TableName: 'orders',
      IndexName: 'customer-index',
      KeyConditionExpression: 'customerId = :id',
      ExpressionAttributeValues: { ':id': customerId }
    };
    
    const result = await dynamodb.query(gsiParams).promise();
    return result.Items;
  } catch (err) {
    if (err.code === 'ResourceNotFoundException') {
      // GSI might be being modified, fallback to full table scan
      const fallbackParams = {
        TableName: 'orders',
        FilterExpression: 'customerId = :id',
        ExpressionAttributeValues: { ':id': customerId }
      };
      
      const result = await dynamodb.scan(fallbackParams).promise();
      return result.Items;
    }
    throw err;
  }
}