Api Rate Abuse in Fiber with Mongodb

Rate abuse in a Fiber API backed by MongoDB typically occurs when an endpoint that queries or writes to MongoDB does not enforce request limits per client identity. Without rate limiting, an unauthenticated or low-identity attacker can issue many requests in a short window, driving high MongoDB operation rates (e.g., find, insert, update), increasing load on the database and potentially degrading availability or enabling data enumeration.

Consider a typical Fiber handler that searches a users collection by email:

app.Get("/users/:email", func(c *fiber.Ctx) error {
    var user bson.M
    collection := database.Collection("users")
    email := c.Params("email")
    if err := collection.FindOne(c.Context(), bson.M{"email": email}).Decode(&user); err != nil {
        return c.Status(fiber.StatusNotFound).JSON(fiber.Map{"error": "user not found"})
    }
    return c.JSON(user)
})

If this endpoint lacks rate limiting, an attacker can rapidly iterate over likely email addresses (e.g., [email protected], [email protected], …) to enumerate users. MongoDB operations themselves are fast, so hundreds of requests per second can be issued, leading to a high number of database reads that may trigger noisy alerts or impact other tenants in a shared cluster. This pattern maps to the BFLA/Privilege Escalation and Rate Limiting checks in middleBrick’s 12 parallel security checks: the scanner tests unauthenticated endpoints for missing rate controls and for IDOR-like behavior where object-level access does not enforce ownership or authorization boundaries.

Moreover, if the same handler performs writes (e.g., creating a password reset token) without rate limits, an attacker can flood the password_resets collection, causing excessive document inserts, storage consumption, and potential denial of service. Because middleBrick’s BFLA/Privilege Escalation and Rate Limiting checks run in parallel, it will flag both the missing ownership/authorization checks and missing rate limits, providing severity, evidence, and remediation guidance in the report.

In practice, the presence of MongoDB as the backend does not introduce a new class of vulnerability, but it amplifies the impact of missing rate controls: each unchecked request results in at least one database operation, and under abuse those operations accumulate quickly. The scanner does not rely on internal architecture, but it validates the observable behavior—an endpoint that allows unlimited requests and interacts with MongoDB is tested for excessive database interactions and enumerated findings under relevant security checks.

To mitigate rate abuse for Fiber endpoints that use MongoDB, enforce per-identity request limits and reduce unnecessary database load. Below are concrete, syntactically correct examples that you can adopt.

1. Global rate limiter with Redis store

Use a Redis-backed rate limiter to apply limits across instances. Here is a complete Fiber example using github.com/gofiber/contrib/redis and github.com/gorilla/mux for a simple token bucket:

package main

import (
    "context"
    "log"
    "time"

    "github.com/gofiber/contrib/redis"
    "github.com/gofiber/fiber/v2"
    "github.com/gorilla/mux"
    "go.mongodb.org/mongo-driver/bson"
    "go.mongodb.org/mongo-driver/mongo"
    "go.mongodb.org/mongo-driver/mongo/options"
)

func main() {
    app := fiber.New()

    // Connect to Redis (adjust address/password as needed)
    r, err := redis.New(redis.Config{
        Server:   "localhost:6379",
        Password: "", // no password
        DB:       0,
    })
    if err != nil {
        log.Fatalf("failed to connect to redis: %v", err)
    }
    app.Use(redis.Middleware(r))

    // MongoDB client setup
    client, err := mongo.Connect(context.TODO(), options.Client().ApplyURI("mongodb://localhost:27017"))
    if err != nil {
        log.Fatalf("failed to connect to mongo: %v", err)
    }
    defer client.Disconnect(context.TODO())
    database := client.Database("mydb")

    // Rate-limited handler: allow 5 requests per minute per IP
    app.Get("/users/:email", func(c *fiber.Ctx) error {
        ip := c.IP()
        limiter := r.Limit(c.Context(), ip, 5, time.Minute)
        if limiter == 0 {
            return c.Status(fiber.StatusTooManyRequests).JSON(fiber.Map{"error": "rate limit exceeded"})
        }
        var user bson.M
        collection := database.Collection("users")
        email := c.Params("email")
        if err := collection.FindOne(c.Context(), bson.M{"email": email}).Decode(&user); err != nil {
            return c.Status(fiber.StatusNotFound).JSON(fiber.Map{"error": "user not found"})
        }
        return c.JSON(user)
    })

    log.Fatal(app.Listen(":3000"))
}

2. Per-user or API-key rate limiting with middleware

If your API uses API keys or authenticated identities, scope limits to that identifier:

func RateLimitByKey(next fiber.Handler) fiber.Handler {
    return func(c *fiber.Ctx) error {
        apiKey := c.Get("X-API-Key")
        if apiKey == "" {
            return c.SendStatus(fiber.StatusUnauthorized)
        }
        // key-based limit: 30 requests per minute
        allowed, err := r.Allow(c.Context(), "rate:apikey:"+apiKey, 30, time.Minute)
        if err != nil || !allowed {
            return c.Status(fiber.StatusTooManyRequests).JSON(fiber.Map{"error": "rate limit exceeded"})
        }
        return next(c)
    }
}

// Usage
app.Get("/data", RateLimitByKey, func(c *fiber.Ctx) error {
    collection := database.Collection("records")
    cursor, err := collection.Find(c.Context(), bson.M{}) // avoid unbounded queries
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": "db error"})
    }
    defer cursor.Close(c.Context())
    var results []bson.M
    if err = cursor.All(c.Context(), &results); err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": "decode error"})
    }
    return c.JSON(results)
})

These examples demonstrate MongoDB-aware remediation: limiting calls before they reach the database and avoiding operations that could cause excessive load. middleBrick’s Rate Limiting and BFLA/Privilege Escalation checks will validate that such controls are present and correctly scoped.