Hallucination Attacks in Gorilla Mux with Mongodb

Gorilla Mux is a popular HTTP request router for Go that enables developers to define URL patterns and associate handlers. When combined with MongoDB as a backend, certain design patterns can expose an API to hallucination attacks, where an attacker manipulates routing or parameter handling to produce inconsistent or fabricated responses.

A hallucination attack in this context occurs when an API returns misleading or invented data by exploiting ambiguous route matching or improper handling of MongoDB query results. For example, if a Gorilla Mux route uses a pattern like /users/{id} and the handler performs a MongoDB query using the id parameter without strict validation, an attacker may supply an id that matches an expected format but maps to no document. If the handler then synthesizes a plausible-looking response using default values or inferred data instead of returning an error, the API hallucinates a result.

This becomes more likely when the handler applies lenient type assertions or omits proper existence checks on the MongoDB cursor. Suppose a handler uses collection.FindOne(context.TODO(), bson.M{"_id": id}).Decode(&user) and, upon an empty result, constructs a user object with placeholder values. To an external caller, the API appears functional while returning data that does not correspond to any stored record. Attackers can leverage this to probe endpoint behavior, infer data models, or conduct reconnaissance for further exploitation.

The interaction between Gorilla Mux routing and MongoDB query logic also enables injection-adjacent hallucination if path or query parameters are concatenated into MongoDB filters without adequate sanitization. An attacker might supply a specially crafted parameter such as {"id": {"$ne": ""}} that bypasses expected matching rules, causing the handler to interpret the filter differently and return an unintended or fabricated subset of data. Because Gorilla Mux does not inherently sanitize inputs, the responsibility falls on the developer to ensure that route parameters are validated before being used in MongoDB queries.

Additionally, hallucination risks increase when multiple routes overlap due to poorly defined matchers. For example, a route defined as /api/v1/users/{id} and another as /api/v1/users/admin can cause the handler to serve unexpected data if the ordering or pattern specificity is not carefully managed. If the handler then executes a MongoDB query based on the captured variable without verifying that the variable aligns with the intended resource, the response may blend real and synthetic content, further eroding trust in the API.

To summarize, hallucination attacks against Gorilla Mux with MongoDB arise from a chain of conditions: ambiguous route definitions, insufficient validation of path parameters, permissive handling of empty query results, and lack of strict schema enforcement. These conditions allow an API to return convincingly structured but factually incorrect data, which can mislead clients, bypass application logic, and expose sensitive inference patterns.

Defending against hallucination attacks when using Gorilla Mux and MongoDB requires explicit handling of query results, strict parameter validation, and consistent error reporting. The following code examples demonstrate secure patterns that eliminate ambiguous responses and ensure that only verified data is returned.

First, always validate route parameters before using them in a MongoDB query. Use Gorilla Mux variables and apply format checks, such as ensuring an id matches expected patterns (e.g., ObjectID format). If the format is invalid, return a 400 error immediately rather than passing the value to MongoDB.

import (
    "net/http"
    "regexp"
    "github.com/gorilla/mux"
    "go.mongodb.org/mongo-driver/bson"
    "go.mongodb.org/mongo-driver/mongo"
)

var idRegex = regexp.MustCompile(`^[0-9a-fA-F]{24}$`)

func getUserHandler(client *mongo.Client) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        vars := mux.Vars(r)
        id := vars["id"]
        if !idRegex.MatchString(id) {
            http.Error(w, `{"error": "invalid user id"}`, http.StatusBadRequest)
            return
        }
        // proceed with MongoDB query
    }
}

Second, explicitly check for the existence of a document and avoid synthesizing data when a query returns no results. Use Decode with error handling and return 404 if the document is not found, preventing the API from inventing placeholder responses.

import (
    "context"
    "encoding/json"
    "net/http"
    "go.mongodb.org/mongo-driver/bson/primitive"
    "go.mongodb.org/mongo-driver/mongo/bson"
)

type User struct {
    ID   primitive.ObjectID `bson:"_id"`
    Name string             `bson:"name"`
}

func getUserByID(client *mongo.Client) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        vars := mux.Vars(r)
        id, err := primitive.ObjectIDFromHex(vars["id"])
        if err != nil {
            http.Error(w, `{"error": "invalid id format"}`, http.StatusBadRequest)
            return
        }
        var user User
        err = client.Database("testdb").Collection("users").
            FindOne(r.Context(), bson.M{"_id": id}).Decode(&user)
        if err == mongo.ErrNoDocuments {
            http.Error(w, `{"error": "user not found"}`, http.StatusNotFound)
            return
        } else if err != nil {
            http.Error(w, `{"error": "internal server error"}`, http.StatusInternalServerError)
            return
        }
        json.NewEncoder(w).Encode(user)
    }
}

Third, ensure that query filters are constructed explicitly rather than through string concatenation or dynamic key insertion that could be influenced by attacker input. Build MongoDB filter documents using typed structures or bson.D to maintain control over the query logic.

func searchUsers(client *mongo.Client) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        email := r.URL.Query().Get("email")
        if email == "" {
            http.Error(w, `{"error": "email parameter required"}`, http.StatusBadRequest)
            return
        }
        filter := bson.D{{"email", email}}
        cursor, err := client.Database("testdb").Collection("users").Find(r.Context(), filter)
        if err != nil {
            http.Error(w, `{"error": "search failed"}`, http.StatusInternalServerError)
            return
        }
        defer cursor.Close(r.Context())
        var results []bson.M
        if err = cursor.All(r.Context(), &results); err != nil {
            http.Error(w, `{"error": "failed to decode results"}`, http.StatusInternalServerError)
            return
        }
        json.NewEncoder(w).Encode(results)
    }
}

Finally, standardize error responses across all handlers so that clients cannot infer the presence or absence of data based on response shape alone when the data is genuinely unavailable. Consistent messaging and status codes reduce the attack surface for probing via hallucination.

• How can I detect hallucination-like behavior during testing of my Gorilla Mux + MongoDB API?

  Use the middleBrick CLI to scan your endpoints: middlebrick scan <url>. The scanner tests unauthenticated attack surfaces and can surface inconsistencies in routing and data fabrication that indicate hallucination risks.

• Does middleBrick provide guidance specific to MongoDB query handling in Gorilla Mux?

  Yes. middleBrick’s findings include remediation guidance aligned with frameworks such as OWASP API Top 10 and include concrete recommendations for parameter validation, result handling, and filter construction in MongoDB-driven Gorilla Mux services.