Api Rate Abuse in Chi with Firestore

Rate abuse in a Chi application backed by Firestore occurs when an attacker sends a high volume of requests that exceed the intended request capacity, potentially degrading performance or increasing costs. Chi is a functional web framework for Clojure, and when endpoints perform read or write operations against Firestore without proper rate controls, the backend can be overwhelmed. Firestore operations, such as document reads, writes, and queries, consume resources and may incur costs per operation. Without rate limiting, a single client can trigger thousands of operations in a short time window, leading to elevated usage and potential service degradation.

In a typical Chi route, handlers directly interact with Firestore using the official client libraries. For example, a REST endpoint that retrieves user data might query a collection for a document based on an identifier provided in the request. If this endpoint is publicly accessible and lacks authentication or rate limiting, an attacker can repeatedly call the endpoint to induce heavy Firestore read traffic. This pattern is especially risky for operations that involve queries with unbounded results or operations that scan large datasets, as each query consumes read capacity and may affect billing.

The exposure is compounded when Firestore security rules are not strictly defined. Overly permissive rules that allow broad read or write access can enable an attacker to perform enumeration or mass retrieval. Even when authentication is present, weak session management or token validation may allow an attacker to reuse credentials across multiple requests. The combination of Chi’s routing behavior and Firestore’s operational model means that rate abuse can manifest both as a denial of service and as a financial impact due to excessive operations.

middleBrick detects rate abuse through its Rate Limiting check, which is part of the 12 parallel security checks. During a scan, the tool evaluates whether endpoints exhibit signs of missing or insufficient rate controls, such as the absence of response headers indicating rate limits or the ability to perform a high number of requests in a short timeframe. This helps identify endpoints where an attacker could exploit Firestore operations without throttling.

To illustrate how an endpoint might be structured, consider a Chi handler that queries Firestore for a user document. The handler uses the Firestore client to fetch data based on a path parameter. Without additional controls, this handler can be invoked repeatedly, leading to a high volume of reads.

(ns example.handler
  (:require [cheshire.core :as json]
            [clj-http.client :as http]
            [firebase-admin.firestore :as firestore]))

(defn get-user-handler [request]
  (let [user-id (get-in request [:params :user-id])
        db (firestore/get-firestore)
        doc-ref (.document db (str "users/" user-id))
        snapshot (.get doc-ref)]
    (if (.exists snapshot)
      {:status 200
       :body (json/generate-string (.getData snapshot))}
      {:status 404
       :body (json/generate-string {:error "User not found"})})))

In this example, each request to /user/:user-id results in a Firestore document read. An attacker can send many requests for different or existing user IDs, causing a high number of read operations. middleBrick’s analysis will highlight the lack of rate limiting as a finding under the Rate Limiting check, emphasizing the need for controls such as request throttling at the Chi handler level or through an API gateway.

Remediation focuses on reducing the rate of Firestore operations per client and ensuring that handlers are resilient against abuse. Implement rate limiting directly in Chi using middleware that tracks request counts per identifier, such as IP address or authenticated user ID. You can use an in-memory store for simplicity in development or a distributed store like Redis in production to coordinate limits across instances.

Additionally, structure Firestore queries to be efficient and bounded. Use document reads instead of collection scans when possible, and apply filters to limit result sets. Ensure security rules restrict access to only necessary paths and operations, preventing unauthorized enumeration.

The following example demonstrates a Chi middleware approach that limits requests per IP address using an atom-based store. This is suitable for single-instance deployments or testing. For distributed environments, integrate with a shared store to synchronize state across nodes.

(ns example.middleware
  (:require [cheshire.core :as json]))

(def rate-limit (atom {}))
(def max-requests 60)
(def window-ms 60000)

(defn rate-limit-middleware [handler]
  (fn [request]
    (let [ip (:remote-addr request)
          now (System/currentTimeMillis)
          entry (get @rate-limit ip)]
      (if entry
        (let [last-time (:last-time entry)
              count (:count entry)
              eligible? (and (>= count max-requests)
                             (< (- now last-time) window-ms))]
          (if eligible?
            {:status 429
             :body (json/generate-string {:error "Rate limit exceeded"})}
            (do
              (swap! rate-limit assoc ip {:last-time now
                                          :count (if (< (- now last-time) window-ms)
                                                   (inc count)
                                                   1)})
              (handler request)))
        (do
          (swap! rate-limit assoc ip {:last-time now :count 1})
          (handler request))))))

(defn app [request]
  [(rate-limit-middleware identity) request])

For Firestore-specific optimizations, consider caching responses for read-heavy endpoints and using batched reads to reduce the number of operations. The following handler shows how to fetch multiple documents in a single logical operation by leveraging parallel calls, reducing overhead compared to sequential reads.

(ns example.handler
  (:require [cheshire.core :as json]
            [firebase-admin.firestore :as firestore]))

(defn get-users-handler [request]
  (let [db (firestore/get-firestore)
        user-ids (get-in request [:params :ids])
        refs (map #(-> db (.document (str "users/" %))) user-ids)
        snapshots (mapv #(.get %) refs)]
    (if (every? #(.exists %) snapshots)
      {:status 200
       :body (json/generate-string (mapv #(.getData %) snapshots))}
      {:status 404
       :body (json/generate-string {:error "One or more users not found"})})))

These examples illustrate how to integrate rate limiting and efficient Firestore access patterns within a Chi application. By combining middleware controls and well-structured data access, you can mitigate rate abuse while maintaining expected functionality.