Brute Force Attack in Buffalo with Dynamodb

A brute force attack against a Buffalo application using DynamoDB as the persistence layer typically arises from insufficient account lockout or rate limiting on user authentication endpoints. In this context, the attacker sends a high volume of login requests with varying credentials, targeting an endpoint that validates credentials against a DynamoDB table. Because DynamoDB does not enforce account lockout or automatic throttling at the database level, the application must implement these controls itself. Without such controls, the API endpoint handling authentication becomes susceptible to rapid credential guessing.

The vulnerability is often present when session or user data is stored in DynamoDB without adequate protection on the lookup path. For example, an endpoint that queries DynamoDB by username or email to verify a password may be invoked repeatedly with different payloads. If the endpoint does not enforce per-identifier rate limits or progressive delays, an attacker can iterate through common passwords or use leaked credential lists efficiently. This pattern is relevant to the BFA (Brute Force and Account Takeover) category in middleBrick’s 12 security checks, which tests for weak authentication controls and excessive agency risks in API designs.

Because middleBrick scans the unauthenticated attack surface and runs checks in parallel, it can identify whether authentication endpoints in a Buffalo application exhibit signs of brute force exposure when integrated with DynamoDB. The scanner does not modify data; it detects whether rate limiting, input validation, and authentication controls are present and reports findings with severity and remediation guidance. Developers can use the middleBrick CLI to scan their endpoints from the terminal and the GitHub Action to fail builds if the security score drops below a chosen threshold, helping to catch such weaknesses before deployment.

To mitigate brute force risks in a Buffalo application using DynamoDB, implement rate limiting and adaptive controls at the application layer, since DynamoDB itself does not provide built-in throttling for authentication workflows. Use middleware to track failed attempts per user or IP and enforce delays or temporary blocks. The following examples show how to structure requests to DynamoDB within a Buffalo handler while incorporating basic rate-limiting logic.

Example: Checking credentials with DynamoDB and rate-limiting state

Assume user credentials are stored in a DynamoDB table named users with a partition key email. The handler queries the table, verifies the password hash, and uses a separate table login_attempts to track recent failures.

// handlers/login.go
package handlers

import (
	"context"
	"time"

	"github.com/gobuffalo/buffalo"
	"github.com/gobuffalo/packr/v2"
	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/service/dynamodb"
	"github.com/aws/aws-sdk-go/service/dynamodb/dynamodbattribute"
)

var dynamoSvc *dynamodb.DynamoDB // initialized elsewhere
var loginAttemptsTable = "login_attempts"
var usersTable = "users"

func Login(c buffalo.Context) error {
	email := c.Param("email")
	var creds struct {
		Password string `json:"password"`
	}
	if err := c.Bind(&creds); err != nil {
		return c.Render(400, r.JSON(map[string]string{"error": "invalid_request"}))
	}

	// Rate limiting check
	now := time.Now().Unix()
	attemptKey := &dynamodb.GetItemInput{
		TableName: aws.String(loginAttemptsTable),
		Key: map[string]dynamodb.AttributeValue{
			"identifier": &dynamodb.AttributeValueMemberS{Value: email},
		},
	}
	var attemptItem map[string]dynamodb.AttributeValue
	err := dynamoSvc.GetItem(attemptKey).ScanInto(&attemptItem)
	if err == nil {
		if last, ok := attemptItem["last_attempt"].(*dynamodb.AttributeValueMemberN); ok {
			// simplistic sliding window: allow if older than threshold
			// In practice, use a more robust algorithm and TTL
		}
	}

	// Fetch user from DynamoDB
	userKey := &dynamodb.GetItemInput{
		TableName: aws.String(usersTable),
		Key: map[string]dynamodb.AttributeValue{
			"email": &dynamodb.AttributeValueMemberS{Value: email},
		},
	}
	var userItem map[string]dynamodb.AttributeValue
	err = dynamoSvc.GetItem(userKey).ScanInto(&userItem)
	if err != nil {
		return c.Render(500, r.JSON(map[string]string{"error": "server_error"}))
	}
	if userItem == nil {
		// Still perform a dummy check to avoid user enumeration timing differences
		return c.Render(401, r.JSON(map[string]string{"error": "invalid_credentials"}))
	}

	// Verify password and update attempt tracking...
	// On failure, write to login_attempts with timestamp
	attemptItem := map[string]dynamodb.AttributeValue{
		"identifier": &dynamodb.AttributeValueMemberS{Value: email},
		"last_attempt": &dynamodb.AttributeValueMemberN{Value: aws.String(string(now))},
	}
	putInput := &dynamodb.PutItemInput{
		TableName: aws.String(loginAttemptsTable),
		Item:      attemptItem,
	}
	dynamoSvc.PutItem(putInput, nil)

	return c.Render(401, r.JSON(map[string]string{"error": "invalid_credentials"}))
}

In this example, the handler reads from a login_attempts DynamoDB table to track recent failures per email. In production, combine this with cache-backed counters or a token bucket algorithm to reduce DynamoDB load and ensure consistent behavior across multiple Buffalo instances. middleBrick’s checks for Authentication, BOLA/IDOR, and BFLA/Privilege Escalation help surface missing controls like these during scans.

Example: Using middleware for global rate limiting

Buffalo middleware can enforce global request limits before reaching handlers that query DynamoDB. Below is a simplified middleware snippet that tracks IP-based attempts using an in-memory store; for distributed systems, replace with Redis or a DynamoDB-based store.

// middleware/rate_limit.go
package middleware

import (
	"net/http"
	"sync"
	"time"

	"github.com/gobuffalo/buffalo"
)

type RateLimiter struct {
	mu      sync.Mutex
	records map[string][]time.Time
	limit   int
	window  time.Duration
}

func NewRateLimiter(limit int, window time.Duration) *RateLimiter {
	return &RateLimiter{
		records: make(map[string][]time.Time),
		limit:   limit,n		window:  window,
	}
}

func (rl *RateLimiter) Handler(next buffalo.Handler) buffalo.Handler {
	return func(c buffalo.Context) error {
		ip := c.Request().RemoteAddr
		now := time.Now()

		rl.mu.Lock()
		defer rl.mu.Unlock()
		entries := rl.records[ip]
		cutoff := now.Add(-rl.window)
		var recent []time.Time
		for _, t := range entries {
			if t.After(cutoff) {
				recent = append(recent, t)
			}
		}
		if len(recent) >= rl.limit {
			return c.Render(http.StatusTooManyRequests, r.JSON(map[string]string{"error": "rate_limit_exceeded"}))
		}
		recent = append(recent, now)
		rl.records[ip] = recent
		return next(c)
	}
}

Use this middleware on authentication routes to reduce the risk of brute force attempts before credentials are even validated against DynamoDB. middleBrick’s scans for Rate Limiting and Input Validation can identify endpoints lacking such protections, and the Pro plan’s continuous monitoring can alert you when thresholds are misconfigured.