Distributed Denial Of Service in Chi with Jwt Tokens

Chi is a functional-first language on the JVM that encourages immutable data and explicit effect handling. When JWT tokens are used for authentication in Chi services, a Distributed Denial of Service (DDoS) risk can emerge from the interplay between token validation overhead, concurrency patterns, and resource exhaustion. Because Chi applications often handle many concurrent requests via lightweight fibers, an endpoint that performs expensive JWT verification—such as parsing and validating signatures or fetching remote JWKS material—can become a bottleneck under high load.

Specifically, if each request triggers a blocking or computationally intensive JWT validation (e.g., repeated RSA verification or network calls to retrieve keys), thread or fiber pools may saturate. This saturation can cause request queuing, increased latency, and ultimately service unavailability, which resembles an application-layer DDoS. Additionally, if the JWT validation logic does not enforce strict timeouts or circuit-breaking, slow or malicious clients submitting crafted tokens can tie up resources, amplifying the impact. In Chi, this is exacerbated when libraries or runtime integrations are not configured for non-blocking I/O or when the token payload is processed in a way that does not leverage Chi’s immutable, persistent data structures efficiently.

Another angle is the exposure of sensitive endpoints without proper rate controls. For example, an unauthenticated endpoint that returns public JWKS material might be scraped repeatedly, consuming bandwidth and CPU. When combined with JWT introspection or validation logic that lacks proper caching, this can create a self-inflicted DDoS scenario where legitimate traffic is delayed or dropped. The risk is not in JWT itself, but in how validation is implemented and integrated into Chi’s concurrency model.

To mitigate DDoS risks in Chi when using JWT tokens, focus on non-blocking validation, caching, and resource isolation. Below are concrete, idiomatic Chi examples that demonstrate safer patterns.

1. Non-blocking JWT validation with timeout

Use asynchronous validation and enforce timeouts to prevent slow tokens from tying up resources. This example uses java.util.concurrent.CompletableFuture via Chi’s Java interop to limit blocking.

(ns myapp.auth
  (:import [java.time Duration]
           [java.util.concurrent CompletableFuture Executors TimeUnit]))

(def executor (Executors/newFixedThreadPool 4))

(defn validate-jwt-async [token]
  (let [fut (CompletableFuture/supplyAsync
             (fn []
               ;; Perform JWT parsing and signature verification here
               ;; e.g., (jwt/verify token public-key)
               {:valid true :payload {}})
             executor)]
    (.get fut 2 TimeUnit/SECONDS))) ;; timeout to avoid hanging

(defn handler [request]
  (let [token (get-in request [:headers "authorization"])
        result (try
                 (validate-jwt-async token)
                 (catch Exception e
                   {:error :validation-failed}))]
    (if (and result (:valid result))
      {:status 200 :body (str "Hello, " (:sub (:payload result)))}
      {:status 401 :body "Unauthorized"})))

2. Cached JWKS retrieval to avoid network saturation

Repeated remote JWKS fetches can overload upstream services and your own network. Cache the JWKS response in memory with a time-based refresh strategy.

(ns myapp.jwks
  (:require [clj-http.client :as client])
  (:import [java.util.concurrent TimeUnit]
           [com.github.benmanes.caffeine.cache Cache Caffeine]))

(def jwks-cache
  (Caffeine/newBuilder
    (-> (Caffeine/create)
        (.expireAfterWrite 5 TimeUnit/MINUTES)
        (.maximumSize 10))
    ^Cache (atom {})))

(defn fetch-jwks []
  (try
    (let [resp (client/get "https://auth.example.com/.well-known/jwks.json"
                           {:as :json :timeout {:socket 2000 :conn 2000}})
          data (:body resp)]
      (reset! jwks-cache data)
      data)
    (catch Exception _
      @jwks-cache))))

(defn public-key-from-jwks [kid]
  (let [jwks (fetch-jwks)]
    (some #(when (= (:kid %) kid) (:x5c %)) jwks)))

3. Rate-limiting and cost-aware validation

Apply per-client rate limits on token validation attempts and avoid expensive operations for suspicious inputs.

(ns myapp.rate
  (:require [com.rpl.specter :refer [select-one]]))

(def rate-limit (atom {}))

(defn allow-request? [client-id]
  (let [now (System/currentTimeMillis)
        window 60000 ;; 1 minute
        limit 100]
    (swap! rate-limit
           (fn [m]
             (let [times (filter #(> (- now %) window) (get m client-id []))]
               (if (>= (count times) limit)
                 (do (reset! rate-limit (dissoc m client-id)) false)
                 (assoc m client-id (conj times now))))))
    (boolean (get @rate-limit client-id))))

(defn guarded-handler [request]
  (let [client-id (get-in request [:headers "x-client-id"])
        token (get-in request [:headers "authorization"])]
    (if (and client-id (allow-request? client-id))
      (handler request)
      {:status 429 :body "Too many requests"})))

4. Prefer RS256 over heavier algorithms and enforce audience/issuer

Choose efficient signature algorithms and validate claims to avoid unnecessary processing and reject malformed tokens early.

(ns myapp.auth.core
  (:require [clj-jwt.core :as jwt]
            [clj-jwt.algorithms :as alg]))

(defn decode-token [token]
  (try
    (jwt/validate token
                  (alg/rs256 (:public-key (fetch-jwks))) ;; use cached keys
                  {:aud "my-api"
                   :iss "https://auth.example.com/"})
    (catch Exception e
      {:error :invalid-token})))