Prompt Injection in Grape with Dynamodb

Prompt injection in a Grape API that uses DynamoDB typically arises when user-controlled input is incorporated into prompts sent to an LLM without validation or isolation. In this stack, a developer might build an endpoint that accepts parameters such as table_name or key_expression, then dynamically construct a prompt to query DynamoDB via an LLM. If the prompt template does not strictly separate instruction from data, an attacker can supply crafted input that alters the intended behavior of the LLM, leading to unintended actions or data leakage.

Consider a Grape route that builds a system prompt including user-supplied filter values before sending the prompt to an LLM to generate a DynamoDB query. Without strict input validation and output encoding, an attacker can inject additional instructions or pseudo-commands that the LLM may interpret as part of the system directive. For example, an input value containing special tokens or newline sequences can shift the role of the LLM from assistant to attacker, causing it to reveal system-level instructions or attempt unauthorized data access patterns.

The risk is compounded because DynamoDB operations are often expressed as structured text or JSON within LLM-generated code. If the LLM output is directly executed without validation, prompt injection can lead to malformed requests or exposure of sensitive item attributes. Attackers may also attempt to infer table structures or item formats by observing error messages returned by the LLM, which can inadvertently disclose metadata about the DynamoDB schema.

middleBrick’s LLM/AI Security checks specifically target this scenario by probing for system prompt extraction, instruction override, and data exfiltration through sequential injection attempts. These tests simulate realistic adversarial inputs against the Grape endpoint, identifying whether user data leaks into the LLM context or whether the model can be forced to reveal its instructions. The scanner also checks for excessive agency patterns, such as unintended use of tool_calls or function_call constructs, which could allow an injected prompt to trigger unauthorized DynamoDB operations.

Because the combination of Grape, DynamoDB, and LLMs amplifies the impact of prompt injection through dynamic prompt assembly and code generation, it is essential to validate and sanitize all inputs, isolate LLM instructions from runtime data, and inspect LLM outputs before they are interpreted as executable logic.

To mitigate prompt injection in a Grape API that interacts with DynamoDB, enforce strict separation between system instructions and dynamic data. Avoid constructing prompts by interpolating user input directly into system messages. Instead, pass user data as explicit parameters that are never part of the instruction context.

Below is a secure example using the aws-sdk-dynamodb gem and a controlled prompt structure. The user-supplied partition_key is used only as a method argument, not embedded in the system prompt:

require 'grape'
require 'aws-sdk-dynamodb'

class MyResource < Grape::API
  format :json

  resource :items do
    desc 'Fetch item with validation'
    params do
      requires :partition_key, type: String, desc: 'Validated partition key'
      requires :sort_key, type: String, desc: 'Validated sort key'
    end

    get ':partition_key' do
      key = params[:partition_key]
      sort = params[:sort_key]

      # Validate format to prevent injection through key values
      unless key.match?(/^\w{1,50}$/) && sort.match?(/^\w{1,100}$/)
        error!('Invalid key format', 400)
      end

      client = Aws::DynamoDB::Client.new(region: 'us-east-1')
      resp = client.get_item({
        table_name: 'ItemsTable',
        key: {
          'pk' => { s: key },
          'sk' => { s: sort }
        }
      })

      present resp.item
    end
  end
end

In this pattern, the LLM is given a static system prompt that never includes user data. If you must use an LLM to generate DynamoDB queries, ensure the LLM receives only sanitized, schema-constrained inputs and that its output is validated against an allowlist of expected operations before any execution attempt.

Additionally, apply output filtering to remove or encode any PII or sensitive keys before returning results. Use middleware in Grape to inspect and log responses without exposing raw DynamoDB metadata. Combine these practices with middleBrick’s continuous monitoring and compliance mapping to OWASP API Top 10 and SOC2 controls, which help track risk trends and enforce security thresholds across your API portfolio.