Api Rate Abuse in Phoenix with Dynamodb

Rate abuse in a Phoenix API backed by DynamoDB typically involves an attacker sending a high volume of legitimate-looking requests that consume provisioned read/write capacity, increase latency, or exhaust reserved throughput. Because DynamoDB enforces limits at the table level (e.g., consumed Read Capacity Units and Write Capacity Units), a flood of requests can trigger throttling (ProvisionedThroughputExceededException) and degrade availability for legitimate users. In Phoenix, this risk is amplified when endpoints perform inefficient queries, scan large tables, or lack per-client or per-tenant rate limits, allowing a single client to saturate shared capacity.

The exposure often stems from two gaps: missing application-layer rate limiting and over-reliance on DynamoDB’s automatic scaling without defensive controls at the API gateway or within Phoenix controllers. For example, an endpoint that queries a DynamoDB table on a non-indexed attribute may generate hot partitions, causing localized throttling even when overall table capacity is under subscription. Attackers can weaponize this by crafting requests that target high-cardinality keys or invoke operations that trigger many strongly consistent reads, which consume capacity more aggressively than eventually consistent reads. Without request validation, filtering, or backpressure mechanisms in Phoenix, the service becomes susceptible to request-flood attacks that manifest as timeouts or service degradation rather than outright HTTP 429 responses.

Because DynamoDB does not provide native request-rate controls tied to individual API keys or users, the responsibility shifts to Phoenix to enforce sensible limits. This includes validating input parameters to avoid expensive queries, applying short caching for repeated reads, and using token-bucket or leaky-bucket algorithms in the application or API gateway. Instrumenting telemetry around consumed capacity and throttle metrics helps detect anomalies early. middleBrick scans can surface missing rate limiting and inefficient DynamoDB access patterns by running checks such as Rate Limiting and Input Validation, and by correlating runtime findings with OpenAPI/Swagger specs that describe expected behavior.

To mitigate rate abuse, implement request validation, caching, and rate limiting in Phoenix before DynamoDB calls. Use input whitelisting and pagination limits to reduce the chance of expensive scans. Apply per-tenant rate limiting and, where possible, leverage DynamoDB adaptive capacity by designing access patterns that avoid hot partitions.

Example 1: Parameter validation and paginated query with strict page size

defmodule MyApp.Users do
  @max_page_size 50

  def list_users(params) do
    with {:ok, limit} <- validate_limit(params["limit"]),
         {:ok, exclusive_start_key} <- validate_exclusive_start_key(params["last_key"]) do
      MyApp.Dynamo.scan("Users", exclusive_start_key, limit)
    else
      {:error, :invalid_limit} -> {:error, :bad_request}
    end
  end

  defp validate_limit(nil), do: {:ok, @max_page_size}
  defp validate_limit(limit) when is_binary(limit) do
    case Integer.parse(limit) do
      {int, ""} when int > 0 and int <= @max_page_size -> {:ok, int}
      _ -> {:error, :invalid_limit}
    end
  end
  defp validate_limit(_), do: {:error, :invalid_limit}

  defp validate_exclusive_start_key(nil), do: {:ok, nil}
  defp validate_exclusive_start_key(key) when is_binary(key) and byte_size(key) <= 2048 do
    {:ok, key}
  end
  defp validate_exclusive_start_key(_), do: {:ok, nil}
end

Example 2: Token-bucket rate limiter with :hammer and DynamoDB conditional writes for distributed tracking

defmodule MyApp.RateLimiter do
  @bucket_size 100
  @refill_interval_ms 60_000

  def allow?(tenant_id) do
    now = System.system_time(:millisecond)
    key = {"rate_limit", tenant_id}

    case MyApp.Dynamo.get_item("RateLimits", %{tenant_id: tenant_id}) do
      %{capacity: cap, last_refill: last} when cap + div(now - last, @refill_interval_ms) * @bucket_size > @bucket_size ->
        # Would exceed bucket; deny
        {:deny, :too_many_requests}
      %{capacity: cap, last_refill: last} ->
        tokens = cap + div(now - last, @refill_interval_ms) * @bucket_size
        if tokens >= 1 do
          update_tokens(key, tokens - 1, now)
          {:allow, %{tokens_remaining: tokens - 1}}
        else
          update_tokens(key, 0, now)
          {:deny, :too_many_requests}
        end
      _ ->
        # First request or missing entry; initialize
        MyApp.Dynamo.put_item("RateLimits", %{tenant_id: tenant_id, capacity: @bucket_size - 1, last_refill: now}, condition: :attribute_not_exists)
        {:allow, %{tokens_remaining: @bucket_size - 1}}
    end
  end

  defp update_tokens(key, tokens, now) do
doc = %{
  update_expression: "SET capacity = :c, last_refill = :t",
  expression_attribute_values: %{c: tokens, t: now}
}
MyApp.Dynamo.update_item("RateLimits", %{tenant_id: elem(key, 1)}, doc)
    end
end

Example 3: Efficient query with GSI and strongly consistent reads only when necessary

defmodule MyApp.Orders do
  def list_recent(user_id, opts) do
    limit = Keyword.get(opts, :limit, 20)
    # Prefer eventually consistent reads for lower capacity cost unless strict freshness required
    consistent = Keyword.get(opts, :consistent, false)
    index_name = "GSI_user_id-index"

    MyApp.Dynamo.query(
      "Orders",
      index_name,
      %{user_id: user_id},
      limit: limit,
      consistent: consistent
    )
  end
end

Example 4: Instrumentation and capacity-aware decisions

defmodule MyApp.Telemetry do
  use Supervisor

  def start_link(_arg) do
    Supervisor.start_link(__MODULE__, :ok, name: __MODULE__)
  end

  def init(:ok) do
    :telemetry.attach("capacity-logger", [:my_app, :dynamo, :capacity], &handle_event/4, nil)
    :ok
  end

  defp handle_event([:my_app, :dynamo, :capacity], measurements, meta, _config) do
    # Send metrics to your monitoring system; trigger alerts on sustained high consumed capacity
    Logger.info("Consumed read capacity: #{measurements[:read]}, write capacity: #{measurements[:write]}")
  end
end

These patterns reduce the surface for rate abuse by constraining payload size and request frequency, avoiding hot keys, and using DynamoDB features appropriately. middleBrick’s checks for Rate Limiting and Input Validation can highlight missing controls, while its OpenAPI/Swagger analysis ensures declared limits align with runtime behavior.