Insufficient Logging in Chi with Mongodb

Insufficient logging in a Chi application that uses Mongodb can weaken visibility into authentication failures, data access, and configuration errors, making it harder to detect tampering or misuse. Chi is a minimalistic routing library for Clojure, and when request handling does not explicitly log authorization outcomes, input validation errors, or database operations, attackers can probe endpoints without generating an audit trail.

Mongodb, as a common backend data store, often handles sensitive user data and operational metadata. If a Chi service writes incomplete or unstructured logs—omitting timestamps, actor identifiers, request IDs, or operation outcomes—an incident response team loses the ability to reconstruct an attack path. For example, a missing log line for a failed login or an unlogged find-one-and-update can allow an attacker to perform brute-force or BOLA/IDOR attempts without detection, because the API’s security checks are not recorded.

In the context of middleBrick’s 12 security checks, insufficient logging intersects with Authentication, Input Validation, and Data Exposure. Without structured logs that include request context and outcome, correlating findings such as unexpected 401/403 responses or anomalous query patterns becomes difficult. This gap is especially risky when combined with permissive Mongodb configurations that allow broad read access, because logs may be the only indicator of unauthorized enumeration or data scraping. The scanner may flag missing auditability as a finding, highlighting the absence of traceable event records for critical flows.

Concrete logging concerns include:

• No log entry for unsuccessful authentication attempts, enabling stealthy credential testing.
• Incomplete Mongodb operation logs that omit filter values or update payloads, reducing forensic value.
• Absence of structured metadata (request ID, user identifier, endpoint) that links application events to API transactions.

To align with best practices, Chi handlers should emit consistent, structured logs around Mongodb interactions—recording success/failure, query filters (excluding sensitive fields), and outcome status. This supports detection of patterns such as rapid sequential failures or unusual query volumes that map to Rate Limiting or Data Exposure checks in automated scans.

Remediation focuses on adding structured, informative logs at key points in Chi request handling and Mongodb operations, while ensuring logs do not expose sensitive data. Use a logging library such as taoensso/timbre to produce consistent log entries, and include correlation identifiers to trace requests across services.

Example: structured logging for authentication and authorization outcomes in a Chi handler:

(ns myapp.handler
  (:require [cheshire.core :as json]
            [compojure.core :refer [GET POST]]
            [compojure.route :as route]
            [org.httpkit.server :refer [run-server]]
            [taoensso.timbre :as log]
            [ring.util.response :as resp]))

(defn wrap-request-logging [handler]
  (fn [request]
    (let [request-id (str (java.util.UUID/randomUUID))]
      (log/info "request-start" {:request-id request-id :method (:request-method request) :uri (:uri request)})
      (try
        (let [response (handler request)]
          (log/info "request-end" {:request-id request-id :status (:status response)})
          response)
        (catch Exception e
          (log/error "request-error" {:request-id request-id :error (.getMessage e)})
          (resp/internal-server-error "An error occurred"))))))

(defn login-handler [{{:keys [username password]} :params}]
  (let [user (mongodb/find-one :users {:username username})]
    (if (and user (verify-password password (:password-hash user)))
      (do
        (log/info "auth-success" {:username username :ip (:remote-addr *request*})
        (resp/response {:token "session-token"}))
        (do
          (log/warn "auth-failure" {:username username :ip (:remote-addr *request*)})
          (resp/unauthorized "Invalid credentials")))))

(defn safe-find-user [request]
  (let [user-id (get-in request [:params :id])
        requester-id (get-in request [:session :user-id])
        user (mongodb/find-one :users {:_id (java.util.UUID/fromString user-id)})]
    (log/debug "data-access" {:requester requester-id :target user-id :collection :users})
    (if (and user (not= user-id requester-id))
      (do
        (log/warn "bola-attempt" {:requester requester-id :target user-id})
        (resp/forbidden "Access denied"))
      (resp/json-response user))))

(defn update-user-handler [request]
  (let [user-id (get-in request [:params :id])
        updates (:body request)
        result (mongodb/update-one :users
                   {:_id (java.util/UUID/fromString user-id)}
                   {:$set (dissoc updates :password)})]
    (log/info "update-result" {:user-id user-id :matched-count (:matched result) :modified-count (:modified result)})
    (resp/json-response result)))

Key practices demonstrated:

• Log at appropriate levels (info for successes, warn for policy violations, error for exceptions).
• Include non-sensitive context (request ID, IP, collection name) without logging full payloads or secrets.
• Log outcomes of database operations (matched/modified counts) to detect unexpected zero-match scenarios that may indicate tampering.
• Use a correlation ID pattern to tie logs to incoming requests, aiding traceability across Chi routes and Mongodb calls.

These changes improve observability and ensure that security-related events—such as authentication attempts, BOLA/IDOR probes, and update operations—are recorded in a structured format that aligns with middleBrick’s findings and supports timely incident response.