Api Rate Abuse in Fiber with Dynamodb

Rate abuse in a Fiber API backed by DynamoDB typically occurs when an endpoint that queries or writes to DynamoDB lacks effective request limiting, allowing a single client to generate excessive and costly operations. DynamoDB charges scale with read/write capacity units and requests, so uncontrolled calls can inflate costs and degrade performance even when the API layer remains responsive. This combination is common in endpoints that accept user-supplied keys or queries that scan large segments of a table, such as searching by a non-partition key or running full scans driven by client parameters.

In a typical Fiber route, developers may map HTTP query parameters directly to DynamoDB key conditions without validating or bounding the caller’s intent. For example, an endpoint like /users/:userId/activity might forward userId as a partition key but also allow open-ended filter parameters (e.g., time ranges, status filters) that cause DynamoDB to consume significant read capacity. Without rate controls, an attacker can craft many legitimate-looking requests that each trigger costly operations, leading to inflated AWS charges and potential throttling from DynamoDB when provisioned capacity is exceeded. The API may still return 200-level responses while the backend experiences high system-level cost and latency, a scenario that middleBrick identifies as a BFLA/Privilege Escalation and Rate Limiting finding.

DynamoDB-specific behaviors amplify the risk. DynamoDB does not natively enforce request-rate caps at the table level; protection must be implemented upstream. A single strongly consistent scan or query with a large filter can consume hundreds of read capacity units, especially on tables with high item sizes. Additionally, DynamoDB’s on-demand mode charges per request, making unthrottled loops particularly expensive. Patterns such as fan-out writes (multiple writes per logical operation) or repeated queries in loops can quickly drive up costs. middleBrick’s checks for Rate Limiting and BFLA/Privilege Escalation highlight these concerns by correlating unauthenticated endpoint behavior with DynamoDB interaction patterns, emphasizing the need for explicit request governance.

To mitigate rate abuse when Fiber interacts with DynamoDB, implement request-level throttling and parameter constraints before issuing any DynamoDB operation. Use middleware to enforce per-client limits and validate inputs to ensure queries are bounded. The following examples show how to integrate rate-limiting logic and safe DynamoDB calls within a Fiber application.

package main

import (
	"context"
	"fmt"
	"log"
	"net/http"
	"time"

	"github.com/gofiber/fiber/v2"
	"github.com/aws/aws-sdk-go-v2/aws"
	"github.com/aws/aws-sdk-go-v2/config"
	"github.com/aws/aws-sdk-go-v2/service/dynamodb"
	"github.com/aws/aws-sdk-go-v2/service/dynamodb/types"
)

// In-memory simple rate limiter keyed by IP for example purposes.
// In production, use a distributed store (e.g., Redis) when running multiple instances.
type rateLimiter struct {
	requests map[string][]time.Time
	max      int
	window   time.Duration
}

func newRateLimiter(max int, window time.Duration) *rateLimiter {
	return &rateLimiter{
		requests: make(map[string][]time.Time),
		max:      max,
		window:   window,
	}
}

func (rl *rateLimiter) allow(key string) bool {
	now := time.Now()
	windowStart := now.Add(-rl.window)
	reqs := rl.requests[key]
	// Remove old requests
	var recent []time.Time
	for _, t := range reqs {
		if t.After(windowStart) {
			recent = append(recent, t)
		}
	}
	if len(recent) >= rl.max {
		return false
	}
	recent = append(recent, now)
	 rl.requests[key] = recent
	return true
}

func main() {
	limiter := newRateLimiter(30, time.Minute) // 30 requests per minute per IP

	app := fiber.New()
	app.Use(func(c *fiber.Ctx) error {
		ip := c.IP()
		if !limiter.allow(ip) {
			return c.Status(http.StatusTooManyRequests).SendString(`{"error":"rate limit exceeded"}`)
		}
		return c.Next()
	})

	app.Get("/users/:userId/activity", func(c *fiber.Ctx) error {
		userId := c.Params("userId")
		// Validate and bound query parameters to avoid expensive scans
		limit := 100
		if l := c.QueryInt("limit"); l > 0 && l <= 1000 {
			limit = l
		}
		ts := c.Query("ts")
		// Ensure query includes a reasonable time window to avoid unbounded scans
		if ts == "" {
			return c.Status(http.StatusBadRequest).SendString(`{"error":"ts query parameter required"}`)
		}

		cfg, err := config.LoadDefaultConfig(context.TODO())
		if err != nil {
			log.Printf("unable to load SDK config, %v", err)
			return c.SendStatus(http.StatusInternalServerError)
		}
		client := dynamodb.NewFromConfig(cfg)

		// Query with partition key and bounded limit; avoid scans where possible
		resp, err := client.Query(context.TODO(), &dynamodb.QueryInput{
			TableName:                 aws.String("UserActivity"),
			KeyConditionExpression:    aws.String("userId = :uid"),
			ExpressionAttributeValues: map[string]types.AttributeValue{":uid": &types.AttributeValueMemberS{Value: userId}},
			Limit:                     int32(limit),
		})
		if err != nil {
			log.Printf("query error, %v", err)
			return c.SendStatus(http.StatusInternalServerError)
		}
		return c.JSON(resp.Items)
	})

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

Key remediation points:

• Apply per-IP or per-api-key rate limits using middleware to cap requests within a time window, preventing cost-driving bursts.
• Enforce strict input validation: require bounded limit parameters and mandatory time-range filters to avoid open-ended or scan-inducing queries.
• Prefer query operations with partition key equality over scans; if scans are necessary, enforce low default limits and require justification.
• Consider using DynamoDB provisioned capacity with alarms to detect abnormal consumption, and evaluate on-demand mode trade-offs under load.

By combining application-level throttling with constrained DynamoDB access patterns, you reduce the surface for rate abuse while maintaining functionality. middleBrick’s continuous monitoring can help detect anomalous request patterns tied to DynamoDB interactions before they escalate.