Cache Poisoning in Hanami with Dynamodb

Cache poisoning in the context of a Hanami application using Amazon DynamoDB occurs when an attacker manipulates data that is subsequently cached, causing malicious or incorrect data to be served to users or downstream services. In Hanami, which encourages explicit domain modeling and use of repositories, this often maps to user-controlled parameters that influence cache keys or cached responses. When those parameters are reflected in DynamoDB queries without strict validation or normalization, an attacker can force the application to store and retrieve attacker-controlled content in the cache layer.

DynamoDB itself does not cache application-level cache entries, but patterns that couple Hanami’s domain logic with DynamoDB responses can lead to unsafe caching. For example, if a Hanami service fetches an item by a sort key derived from unvalidated user input (e.g., params[:id]), and then caches the result using that same input as part of the cache key, an attacker can craft inputs that modify cache keys or store objects under keys that other users inadvertently read. This is particularly risky when cache keys incorporate DynamoDB attribute values directly without canonicalization or serialization safeguards.

Consider a Hanami endpoint that retrieves a product by a URL-friendly slug and uses it to query a DynamoDB table where the partition key is product_slug. If the slug is not strictly validated and is used verbatim in the cache key, an attacker could provide a slug such as ../../../admin or a specially crafted string that results in cache collisions. If the caching layer is shared (e.g., a Redis or in-memory cache), the poisoned entry may be returned to other users, exposing sensitive information or causing incorrect business logic execution. Moreover, DynamoDB responses may include sensitive metadata or attributes that, when cached without sanitization, lead to data exposure in subsequent requests.

In Hanami, the risk is compounded when query results are cached across different contexts (web, API, background jobs). An attacker leveraging input that maps to DynamoDB key attributes can force the cache to store one user’s data under another user’s cache key, effectively creating a confused deputy scenario. This aligns with the broader BOLA/IDOR category, where lack of proper authorization checks on identifiers leads to cross-object access. Because Hanami often uses value objects and immutable entities, cached representations that include DynamoDB entity attributes must be carefully scoped and validated to prevent cache poisoning.

To detect such issues, middleBrick performs 12 security checks in parallel, including Input Validation, Property Authorization, and BOLA/IDOR, alongside an OpenAPI/Swagger spec analysis with full $ref resolution that cross-references spec definitions with runtime findings. This helps identify unsafe parameter handling and missing authorization boundaries that could enable cache poisoning in Hanami applications integrated with DynamoDB.

Remediation focuses on strict input validation, canonical cache keys, and scoping cached data to the correct tenant or user context. In Hanami, leverage value objects and entity mappings to ensure DynamoDB query parameters are normalized and validated before being used in cache keys or queries.

First, define a value object for identifiers that enforces allowed characters and length. For example, a product slug should be restricted to alphanumeric characters and hyphens:

module App::Types
  class SafeSlug < Hanami::Types::String
    configure do
      pattern? _safe_slug
    end
  end
end

# Regex for slug: lowercase alphanumeric and hyphens only
PATTERN = /\A[a-z0-9\-]+\z/.freeze
def _safe_slug?(value)
  value =~ PATTERN
end

When querying DynamoDB, use this validated slug rather than raw user input. A Hanami repository might look like this:

class ProductRepository
  def self.find(slug)
    # slug is already validated by the command or value object
    response = App::Container['persistence.db'].client.get_item(
      table_name: 'products',
      key: {
        product_slug: { s: slug }  # slug is a SafeSlug instance
      }
    )
    response.item ? Product.new(response.item) : nil
  end
end

For caching, ensure the cache key incorporates a namespaced scope and the validated slug, avoiding direct exposure of DynamoDB key attributes. Use a deterministic, serialized representation:

def cache_key(safe_slug, tenant_id)
  "tenant:#{tenant_id}:product:#{safe_slug}"
end

# In a Hanami service or action
product = cache.fetch(cache_key(safe_slug, current_tenant.id)) do
  ProductRepository.find(safe_slug)
end

Additionally, apply attribute-level filtering when storing DynamoDB responses in cache to remove sensitive fields that should not be cached, such as internal metadata or IAM-related attributes. If using a JSON-based cache store, explicitly permit only safe fields:

def cached_product_data(item)
  {
    id: item['id'],
    name: item['name'],
    price: item['price'],
    slug: item['product_slug']
  }
end

Finally, integrate these patterns with middleBrick’s checks — such as Input Validation, Property Authorization, and BOLA/IDOR — to ensure your Hanami application’s use of DynamoDB remains secure. The Pro plan’s continuous monitoring can help detect regressions in cache behavior, while the CLI allows you to script scans and fail CI/CD builds if risk thresholds are exceeded.