Credential Stuffing in Echo Go with Dynamodb

Credential stuffing is an automated attack where valid credentials from one breach are systematically tried against a login endpoint. When an Echo Go service uses DynamoDB as its user store without protective controls, the combination can amplify risk. Echo Go’s idiomatic HTTP routing and structured handlers can inadvertently enable high-volume, low-latency requests to authentication routes if rate limiting is absent or misconfigured. DynamoDB, often chosen for its scalability and flexible schema, may store user credentials with insufficient protections such as missing rate limits on API-level queries, weak or missing multi-factor authentication, and overly permissive IAM policies that allow broad read access to the user table.

In this stack, credential stuffing leverages two weaknesses: (1) an authentication endpoint that does not enforce per-user or per-IP attempt limits, and (2) DynamoDB access patterns that permit rapid querying by username or email without throttling or anomaly detection. Because DynamoDB does not inherently enforce request-rate caps at the table level, an attacker can issue many concurrent queries that each return quickly, enabling large-scale enumeration. If the Echo Go application does not mask whether a username exists and does not enforce captchas or progressive delays after failures, the attacker can harvest valid accounts without triggering defensive responses. The risk is compounded when the service uses unauthenticated or weakly scoped AWS credentials, allowing an attacker to probe the DynamoDB table directly.

Echo Go’s middleware chain plays a critical role. If authentication handlers log raw credentials or pass them through uncontrolled channels, they may leak via logs or error messages, aiding further exploitation. DynamoDB’s conditional writes and optimistic locking can mitigate some race conditions, but they must be explicitly implemented. Without these controls, an attacker can conduct credential stuffing with minimal noise, especially when requests originate from distributed sources that bypass IP-based blacklists. The scan coverage of middleBrick includes authentication checks and rate limiting assessments, which can surface these gaps in the unauthenticated attack surface of an Echo Go + DynamoDB deployment.

Remediation centers on reducing the effectiveness of automated login attempts and hardening access to DynamoDB. Implement per-user and per-IP rate limiting, account lockout with exponential backoff, and strict IAM policies. Ensure that authentication responses use constant-time comparisons and do not reveal account existence.

• Rate limiting at the Echo Go middleware layer:

package main

import (
    "net/http"
    "time"

    "github.com/gorilla/handlers"
)

// RateLimit middleware limits requests per IP.
func RateLimit(next http.Handler) http.Handler {
    visits := make(map[string]int)
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        ip := r.RemoteAddr
        if visits[ip] > 100 {
            http.Error(w, "rate limit exceeded", http.StatusTooManyRequests)
            return
        }
        visits[ip]++
        time.AfterFunc(time.Minute, func() {
            visits[ip]--
        })
        next.ServeHTTP(w, r)
    })
}

• Secure DynamoDB query with condition and least privilege:

package main

import (
    "context"
    "fmt"
    "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"
)

type UserRepository struct {
    client *dynamodb.Client
    table  string
}

func NewUserRepository() *UserRepository {
    cfg, _ := config.LoadDefaultConfig(context.TODO())
    return &UserRepository{
        client: dynamodb.NewFromConfig(cfg),
        table:  "users",
    }
}

// GetUserByEmail retrieves a user with a condition to avoid scanning the whole table.
func (r *UserRepository) GetUserByEmail(ctx context.Context, email string) (map[string]types.AttributeValue, error) {
    // Assume GSI on email for efficient lookup
    out, err := r.client.Query(ctx, &dynamodb.QueryInput{
        TableName: aws.String(r.table),
        IndexName: aws.String("EmailIndex"),
        KeyConditionExpression: aws.String("email = :e"),
        ExpressionAttributeValues: map[string]types.AttributeValue{
            ":e": &types.AttributeValueMemberS{Value: email},
        },
        Limit: aws.Int32(1),
    })
    if err != nil {
        return nil, fmt.Errorf("query failed: %w", err)
    }
    if len(out.Items) == 0 {
        return nil, fmt.Errorf("user not found")
    }
    return out.Items[0], nil
}

• IAM policy example limiting DynamoDB access to specific actions and conditions:

{ 
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "dynamodb:Query",
                "dynamodb:GetItem"
            ],
            "Resource": "arn:aws:dynamodb:region:account-id:table/users",
            "Condition": {
                "ForAllValues:StringEquals": {
                    "dynamodb:LeadingKeys": ["${aws:username}"]
                }
            }
        }
    ]
}

• Constant-time comparison for authentication to prevent timing attacks:

package main

import (
    "crypto/subtle"
)

func safeCompare(stored, provided string) bool {
    return subtle.ConstantTimeCompare([]byte(stored), []byte(provided)) == 1
}

• Progressive delays after failed attempts to deter bulk submissions:

package main

import (
    "sync"
    "time"
)

type LockoutStore struct {
    mu      sync.Mutex
    attempts map[string]int
    resets  map[string]time.Time
}

func (s *LockoutStore) RecordFailure(ident string) {
    s.mu.Lock()
    defer s.mu.Unlock()
    s.attempts[ident]++
    if s.attempts[ident] >= 5 {
        s.resets[ident] = time.Now().Add(15 * time.Minute)
    }
}

func (s *LockoutStore) IsLocked(ident string) bool {
    s.mu.Lock()
    defer s.mu.Unlock()
    if until, ok := s.resets[ident]; ok && time.Now().Before(until) {
        return true
    }
    return false
}

These changes align with the checks in middleBrick’s authentication, rate limiting, and data exposure categories, helping reduce the credential stuffing risk surface for an Echo Go service backed by DynamoDB.