HIGH injection flawsgrapedynamodb

Injection Flaws in Grape with Dynamodb

Injection Flaws in Grape with Dynamodb — how this specific combination creates or exposes the vulnerability

Grapes API framework for Ruby does not validate or sanitize user input before it is used to construct DynamoDB operations. When untrusted parameters such as id, user_id, or sort_key are directly interpolated into key condition expressions, attribute names, or scan filters, injection flaws emerge. DynamoDB itself does not support SQL, but injection manifests through malformed condition expressions, unsafe attribute names, and uncontrolled filter logic, leading to BOLA/IDOR, data exposure, or privilege escalation.

For example, accepting a partition key from params without validation can allow an attacker to access any item by changing the key value, bypassing ownership checks. Similarly, dynamic attribute names built from user input can enable Property Authorization issues, where a user modifies the field they are not permitted to read or write. These patterns violate the principle of least privilege and amplify the impact of weak authorization controls.

MiddlewareBrick scans the unauthenticated attack surface of a Grape endpoint and detects these patterns across 12 security checks, including Input Validation, Property Authorization, and BOLA/IDOR. By correlating OpenAPI specifications with runtime behavior, it identifies unsafe usage such as unsanitized key condition expressions, missing ownership scopes, and missing length or format constraints that make injection feasible.

Consider a vulnerable endpoint that constructs a DynamoDB query from request parameters without sanitization:

post '/items/:id' do
  key = { partition_key: params[:id] }
  item = dynamodb.get_item(table_name: 'Items', key: key)
  present item
end

If params[:id] is not constrained, an attacker can supply arbitrary partition key values and retrieve items belonging to other users. A safer approach validates and scopes the input to the requesting user, ensuring the key aligns with the authenticated identity and enforces BOLA protections.

Dynamic filter expressions compound the risk. An endpoint that builds a FilterExpression from user-controlled strings can unintentionally expose data or bypass intended constraints:

post '/search' do
  filter = "attribute_exists(#{params[:filter_field]})"
  response = dynamodb.scan(table_name: 'Logs', filter_expression: filter)
  present response
end

Here, an attacker can manipulate filter_field to probe schema details or trigger excessive scans. Injection flaws in this context are not about SQL but about unsafe composition of expression strings and attribute names, which can lead to Data Exposure and Excessive Agency.

Dynamodb-Specific Remediation in Grape

Remediation centers on strict validation, parameterization, and scoping. Never directly interpolate user input into key maps, condition expressions, or attribute names. Use AWS SDK constructs as intended, and enforce ownership at the data access layer.

Validate and scope partition keys to the authenticated user. Instead of using raw params, map the user to their owned partition key and reject any mismatch:

helpers do
  def current_user_partition_key
    # Ensure the key belongs to the authenticated identity
    user = current_user
    "user_#{user.id}"
  end
end

post '/items/:id' do
  provided_key = { partition_key: params[:id] }
  expected_key = { partition_key: current_user_partition_key }
  unless provided_key == expected_key
    halt 403, { error: 'Forbidden' }.to_json
  end
  item = dynamodb.get_item(table_name: 'Items', key: expected_key)
  present item
end

For queries and scans, avoid building expressions via string interpolation. Use expression attribute names and values to keep structure and data separate:

post '/logs' do
  response = dynamodb.scan(
    table_name: 'Logs',
    filter_expression: '#attr = :val',
    expression_attribute_names: { '#attr' => 'status' },
    expression_attribute_values: { ':val' => 'active' }
  )
  present response
end

When attribute names must be dynamic, validate them against a strict allowlist:

ALLOWED_FIELDS = %w[status created_at owner]

post '/search' do
  field = params[:field]
  halt 400, { error: 'Invalid field' }.to_json unless ALLOWED_FIELDS.include?(field)
  filter = "attribute_exists(#{field})"
  response = dynamodb.scan(
    table_name: 'Logs',
    filter_expression: filter
  )
  present response
end

Enforce ownership in scans and queries by appending a condition on the partition key or a user_id attribute:

get '/records' do
  response = dynamodb.scan(
    table_name: 'Records',
    filter_expression: 'user_id = :uid',
    expression_attribute_values: { ':uid' => current_user.id }
  )
  present response
end

Use middleware or before blocks in Grape to centralize validation and scoping. This reduces duplication and ensures every data access respects authorization boundaries.

For comprehensive coverage, integrate MiddlewareBrick into your workflow. Use the CLI to scan endpoints from the terminal:

middlebrick scan https://api.example.com/openapi.json

Or add the GitHub Action to fail builds when risk scores degrade, ensuring injection issues are caught before deployment. The MCP Server enables scanning directly from AI coding assistants in your IDE, providing rapid feedback during development.

Frequently Asked Questions

Can DynamoDB injection lead to privilege escalation in Grape APIs?
Yes. If user input is used to construct key conditions, expression attribute names, or scan filters without validation, an attacker can bypass ownership checks, access other users' data, or trigger operations that exceed intended permissions, effectively enabling privilege escalation.
What is the most effective mitigation for DynamoDB injection in Grape?
Strict input validation, scoping queries to the authenticated user, using expression attribute names and values instead of string interpolation, and maintaining an allowlist for dynamic attribute names. Centralizing validation in before blocks reduces inconsistencies across endpoints.