Cache Poisoning in Dynamodb

Cache poisoning in DynamoDB occurs when an attacker manipulates cached data to return malicious or incorrect responses to legitimate users. This vulnerability is particularly dangerous in DynamoDB environments because of the service's eventual consistency model and the common practice of caching query results to reduce read capacity unit (RCU) consumption.

The most common attack pattern involves exploiting DynamoDB's ConsistentRead parameter. When applications cache query results without proper validation, an attacker can trigger a write operation that propagates to the cache before DynamoDB's eventual consistency catches up. Subsequent reads from the cache return the poisoned data until the cache expires or is invalidated.

Consider this vulnerable pattern:

def get_user_by_email(email):
    cache_key = f"user:{email}"
    cached = cache.get(cache_key)
    if cached:
        return cached  # Returns potentially poisoned data
    
    # Query without consistent read
    result = dynamodb_table.query(
        KeyConditionExpression=Key('email').eq(email),
        Limit=1
    )
    
    if result['Items']:
        cache.set(cache_key, result['Items'][0], ttl=300)
    return result['Items'][0] if result['Items'] else None

An attacker can exploit this by triggering a write operation that creates a conflicting item, causing DynamoDB's eventual consistency to return different results across nodes. The cache stores the first response it receives, which might be manipulated data.

Another attack vector involves DynamoDB's UpdateItem operations with conditional writes. If an application caches the result of an update without verifying the conditional write succeeded, an attacker can cause the cache to store a failed operation's response, leading to data integrity issues.

Batch operations present additional risks. When using BatchWriteItem or BatchGetItem, partial failures can leave caches in inconsistent states. If an application caches the successful portion of a batch operation without handling failures properly, it creates opportunities for cache poisoning.

Time-based attacks are particularly effective against DynamoDB. By timing write operations during peak load when DynamoDB's replication lag increases, attackers can increase the window during which poisoned data remains in the cache.

Detecting cache poisoning in DynamoDB requires monitoring both the cache layer and DynamoDB's behavior. The key indicators include inconsistent read responses, unexpected cache hits with stale data, and timing anomalies in write operations.

middleBrick's DynamoDB security scanning specifically targets these vulnerabilities through several detection mechanisms:

Cache Consistency Validation: The scanner tests whether your application properly validates cached data against DynamoDB's current state. It simulates write operations and verifies that subsequent reads reflect the most recent data, not cached responses.

Conditional Write Verification: middleBrick checks if your application verifies the success of conditional writes before caching results. It tests scenarios where conditional writes fail but the application still caches the attempted response.

Batch Operation Integrity: The scanner examines how your application handles partial failures in batch operations. It verifies that caches are only updated when entire batch operations succeed, preventing partial poisoning.

Consistency Model Testing: middleBrick tests your application's behavior under DynamoDB's eventual consistency model by triggering writes and measuring the time until consistent reads return. Applications that cache without proper consistency checks will show vulnerability.

TTL and Invalidation Analysis: The scanner evaluates your cache invalidation strategies, checking whether Time-To-Live (TTL) values are appropriately configured and whether manual invalidation occurs when data changes.

Here's how middleBrick reports DynamoDB cache poisoning vulnerabilities:

{
  "category": "Data Exposure",
  "severity": "High",
  "finding": "Cache poisoning vulnerability in DynamoDB query caching",
  "description": "Application caches DynamoDB query results without consistent read validation",
  "remediation": "Implement consistent reads for cached queries and validate cache freshness before serving cached data",
  "impact": "Attackers can manipulate cached responses, leading to data integrity issues and potential information disclosure"
}

The scanner also provides DynamoDB-specific configuration analysis, checking for proper use of ConsistentRead parameters, appropriate RCU provisioning to prevent throttling that could lead to inconsistent caching behavior, and correct partition key design to avoid hot partition issues that exacerbate consistency problems.

Remediating cache poisoning in DynamoDB requires a multi-layered approach that addresses both the caching strategy and DynamoDB's consistency model. Here are specific, code-level solutions for DynamoDB environments:

Implement Consistent Reads for Cached Queries: Always use ConsistentRead=True when reading data that might be cached or when data consistency is critical.

def get_user_by_email(email):
    cache_key = f"user:{email}"
    if cached:
        # Verify cached data is still valid
        current_version = dynamodb_table.get_item(
            Key={'email': email},
            ConsistentRead=True
        )
        if current_version['Item']['version'] == cached['version']:
            return cached
        else:
            cache.delete(cache_key)
    
    # Query with consistent read
    result = dynamodb_table.query(
        KeyConditionExpression=Key('email').eq(email),
        ConsistentRead=True,
        Limit=1
    )
    
    if result['Items']:
        cache.set(cache_key, result['Items'][0], ttl=300)
    return result['Items'][0] if result['Items'] else None

Version-Based Cache Validation: Implement optimistic locking with version numbers to detect when cached data becomes stale.

def update_user_email(user_id, new_email):
    while True:
        # Get current item with version
        item = dynamodb_table.get_item(Key={'id': user_id})
        if 'Item' not in item:
            return False
        
        # Attempt conditional update with version check
        response = dynamodb_table.update_item(
            Key={'id': user_id},
            UpdateExpression="set email = :e, version = version + 1",
            ConditionExpression="version = :v",
            ExpressionAttributeValues={':e': new_email, ':v': item['Item']['version']},
            ReturnValues="ALL_NEW"
        )
        
        # Invalidate cache with new version
        cache.delete(f"user:{user_id}")
        return response['Attributes']

Cache Invalidation on Write Operations: Always invalidate or update relevant cache entries when performing write operations.

def delete_user(user_id):
    # Delete from DynamoDB
    dynamodb_table.delete_item(Key={'id': user_id})
    
    # Invalidate all related cache entries
    cache.delete(f"user:{user_id}")
    cache.delete(f"user-posts:{user_id}")
    cache.delete(f"user-profile:{user_id}")

Implement Cache Entry Timeouts Based on Data Volatility: Configure TTL values based on how frequently data changes rather than using arbitrary timeouts.

def get_cache_ttl(data_type, volatility_factor=2):
    volatility = {
        'user_profile': 3600,      # 1 hour
        'user_activity': 300,      # 5 minutes
        'user_settings': 7200      # 2 hours
    }
    return volatility.get(data_type, 300) * volatility_factor

Monitor DynamoDB Metrics for Early Detection: Set up CloudWatch alarms for ConditionalCheckFailedRequests, ThrottledRequests, and ReplicationLatency to detect potential cache poisoning scenarios.

import boto3

cloudwatch = boto3.client('cloudwatch')

def setup_cache_poisoning_alarms():
    cloudwatch.put_metric_alarm(
        AlarmName='CachePoisoningRisk-ConditionalFailures',
        MetricName='ConditionalCheckFailedRequests',
        Namespace='AWS/DynamoDB',
        Statistic='Sum',
        Period=300,
        EvaluationPeriods=1,
        Threshold=10,
        ComparisonOperator='GreaterThanThreshold'
    )

Implement Read-Your-Write Consistency: For applications requiring strong consistency, use DynamoDB's ConsistentRead parameter and implement cache-aside patterns that always verify data freshness.

def get_consistent_user_data(email):
    cache_key = f"user:{email}"
    
    # Always perform a consistent read to verify cache
    current_data = dynamodb_table.get_item(
        Key={'email': email},
        ConsistentRead=True
    )
    
    if cached and cached == current_data['Item']:
        return cached
    
    # Cache the verified data
    cache.set(cache_key, current_data['Item'], ttl=300)
    return current_data['Item']