Api Key Exposure in Gorilla Mux with Mongodb

When an API built with Gorilla Mux stores or references an API key in a MongoDB document, several risk patterns can unintentionally expose that key. This typically occurs when route handlers retrieve a document from MongoDB and serialize the full document—including fields like api_key or secret—into JSON responses, logs, or error messages. Because Gorilla Mux uses path variables and query parameters to route requests, a handler may inadvertently include sensitive fields when constructing responses or passing data to downstream services.

For example, a common pattern is to look up an API key from MongoDB based on a client-supplied identifier (such as an api_key_id path parameter). If the handler does not explicitly exclude sensitive fields before encoding the result as JSON, an attacker who knows or guesses the identifier can retrieve the full document and obtain the raw key. This becomes more likely when the MongoDB document contains nested fields or when debugging information is merged into the response struct.

Another vector arises from logging and error handling. If your Gorilla Mux handlers log the MongoDB document—intentionally or unintentionally—during request processing, the API key may appear in log aggregation systems. Similarly, stack traces or HTTP 500 responses that include the MongoDB document can surface the key to clients when structured error details are returned without scrubbing.

The scanning methodology used by tools like middleBrick reflects these risks by checking for data exposure through unauthenticated endpoints and cross-referencing OpenAPI specifications with runtime behavior. For instance, if an OpenAPI spec defines a response schema that includes fields such as api_key, and the runtime behavior returns that field, the scan will flag it as a data exposure finding. This detection does not imply that middleBrick fixes the issue; it highlights where sensitive data may leave your service in this specific stack combination.

Additionally, because Gorilla Mux routes are matched based on patterns, misconfigured route precedence can cause a handler intended for administrative operations to be invoked in contexts where sensitive data should not be returned. If such a handler returns a MongoDB document containing keys, the exposure risk increases. MiddleBrick’s checks for BOLA/IDOR and Property Authorization help surface these routing and authorization gaps by correlating endpoint definitions with observed responses.

Finally, the presence of an unauthenticated LLM endpoint in your service surface can amplify the impact if error messages or logs containing API keys are reflected in responses used by AI tooling. MiddleBrick’s LLM/AI Security checks specifically scan for system prompt leakage and output exposure, which can catch cases where API keys appear in model outputs or injected prompts due to improper handling of MongoDB-derived data.

To prevent API key exposure when using Gorilla Mux with MongoDB, you should explicitly control which fields are serialized and ensure sensitive values are never included in responses or logs. Below are concrete, realistic code examples that demonstrate secure handling patterns.

First, define a response structure that excludes sensitive fields. Instead of returning the raw MongoDB document, create a dedicated struct for your API responses.

// models.go
package main

import "go.mongodb.org/mongo-driver/bson/primitive"

type APIKeyRecord struct {
    ID        primitive.ObjectID `bson:"_id" json:"-"`
    APIKey    string             `bson:"api_key" json:"-"`
    CreatedAt time.Time          `bson:"created_at" json:"created_at"`
}

// apikey_response.go
package main

type APIKeyResponse struct {
    ID        string `json:"id"`
    CreatedAt string `json:"created_at"`
}

func toAPIKeyResponse(doc APIKeyRecord) APIKeyResponse {
    return APIKeyResponse{
        ID:        doc.ID.Hex(),
        CreatedAt: doc.CreatedAt.Format(time.RFC3339),
    }
}

In your Gorilla Mux handler, use the response struct and avoid passing the full MongoDB document to the JSON encoder.

// handlers.go
package main

import ("net/http" "go.mongodb.org/mongo-driver/bson" "go.mongodb.org/mongo-driver/mongo")

func GetAPIKeyHandler(client *mongo.Client) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        vars := mux.Vars(r)
        id := vars["id"]
        var doc APIKeyRecord
        err := client.Database("appdb").Collection("api_keys").FindOne(r.Context(), bson.M{"_id": id}).Decode(&doc)
        if err != nil {
            http.Error(w, "not found", http.StatusNotFound)
            return
        }
        response := toAPIKeyResponse(doc)
        w.Header().Set("Content-Type", "application/json")
        json.NewEncoder(w).Encode(response)
    }
}

Second, ensure that logging never includes the API key. When logging request context or database operations, explicitly exclude sensitive fields.

// logger.go
package main

import "log"

func LogRequestWithoutKey(r *http.Request, status int) {
    log.Printf("method=%s path=%s status=%d", r.Method, r.URL.Path, status)
}

Third, validate and sanitize any data derived from MongoDB before it is used in error messages or structured outputs. Do not rely on the default behavior of struct embedding or the MongoDB driver’s document-to-JSON conversion, as these can expose fields tagged json:"-" only during direct encoding if misused.

Finally, consider using field-level encryption or environment-managed secrets for the actual API key values stored in MongoDB, and restrict database user permissions to the minimum required. These practices complement the application-level controls and reduce the impact of any accidental exposure.