Crlf Injection in Grape with Dynamodb

Crlf Injection occurs when an attacker can inject a Carriage Return (CR, \r) and Line Feed (\n) sequence into a header or log entry, causing the application to split and falsify records. In a Grape API that uses Amazon DynamoDB, this typically arises when user-controlled input is reflected in HTTP response headers, DynamoDB item attributes that are later used for logging, or when constructing values stored in DynamoDB that later become part of log lines or header values.

For example, a Grape endpoint that accepts a user-supplied X-Custom-Header parameter and stores it as a DynamoDB attribute can inadvertently store the raw input. Later, when the application reads that attribute and writes it into an HTTP response header or log formatter, the embedded \r\n sequences can cause header splitting. In DynamoDB, a string attribute like User-Agent: curl/7.68.0\r\nX-Injected: 1 is stored as-is, but when that value is later rendered in a header or log, it can split responses or forge log entries, bypassing intended log separation or enabling cache poisoning.

Because DynamoDB is a NoSQL database, injection here is not SQL injection; it is about how controlled values are interpreted downstream. When combined with Grape’s flexible header handling, a vulnerable pattern looks like reading a DynamoDB item and directly setting headers without validation:

# Vulnerable: header value taken directly from DynamoDB item response
get '/profile' do
  user_id = params[:id]
  item = dynamodb.get_item(table_name: 'Users', key: { 'id' => { s: user_id } })
  header 'X-User-Data', item['attributes']['custom_header_value'].s
  { status: item['attributes']['status'].s }.to_json
end

If the DynamoDB attribute custom_header_value contains foo\r\nX-Controlled: true, the resulting HTTP response will include an additional header X-Controlled: true, which may expose internal routing or override intended behavior.

Additionally, Crlf Injection can affect logging and monitoring integrations. When DynamoDB streams or application logs record item attributes that contain injected sequences, log parsers may misinterpret entries, leading to incorrect metrics or obscured audit trails. This is especially relevant when logs are aggregated and line-based delimiters are expected to separate records cleanly.

Remediation focuses on two areas: sanitization of data before it is stored or rendered, and strict validation of any value that will be used in headers or logs. For DynamoDB, ensure that attributes which may be used in HTTP contexts are normalized to remove CR and LF characters before insertion or retrieval.

Use a sanitization helper when writing to DynamoDB and when reading for header/log output. For example, define a filter that removes or replaces control characters, and apply it consistently:

# Safe: sanitize before storing to DynamoDB
def sanitize_header_value(value)
  return nil if value.nil?
  value.gsub(/[\r\n]/, '')
end

post '/profile' do
  user_id = params[:id]
  custom_value = sanitize_header_value(params[:custom_header_value])
  dynamodb.put_item(
    table_name: 'Users',
    item: {
      'id' => { s: user_id },
      'custom_header_value' => { s: custom_value },
      'status' => { s: 'active' }
    }
  )
  { message: 'updated' }.to_json
end

When reading from DynamoDB for header usage, apply the same sanitization to ensure no stored value can later cause injection:

# Safe: sanitize on retrieval before using in headers
get '/profile' do
 user_id = params[:id]
  item = dynamodb.get_item(table_name: 'Users', key: { 'id' => { s: user_id } })
  safe_value = sanitize_header_value(item['attributes']['custom_header_value']&[])
  header 'X-User-Data', safe_value if safe_value
  { status: item['attributes']['status'].s }.to_json
end

For logging, configure your formatter to handle or reject lines containing CR/LF, or replace them with a safe placeholder. MiddleBrick can be used to validate that endpoints do not reflect unsanitized DynamoDB attributes in headers by running a scan and reviewing findings related to header injection and data exposure checks.

In the Pro plan, continuous monitoring can be enabled to detect patterns where DynamoDB-stored values are reflected in HTTP headers over time, providing automated alerts when new endpoints introduce risky behavior. The GitHub Action can also enforce that any code referencing DynamoDB header usage passes linting rules that require sanitization functions to be present.