Password Spraying in Fiber with Dynamodb

Password spraying is an authentication attack that attempts a small number of common passwords across many accounts to avoid account lockouts. When a Fiber application uses Amazon DynamoDB as its user store, the interaction between the HTTP request handling in Fiber and the eventual consistency and query patterns in DynamoDB can expose timing differences and error handling behaviors that aid an attacker.

In a typical Fiber-based API, a login route queries DynamoDB by a username or email to retrieve a user record and then compares the provided password with a stored hash. If the implementation does not standardize response times and error messages, an attacker can infer whether a username exists based on subtle latency differences or distinct HTTP status codes. DynamoDB’s provisioned or on-demand capacity modes can also introduce variable response latencies under load, which may amplify timing discrepancies between existing and non-existing users.

Moreover, because DynamoDB does not enforce account lockout at the service level, the application must implement rate-limiting and monitoring. Without robust global rate limiting, a sustained password spraying campaign can generate many requests that appear as legitimate traffic. The scan checks for Authentication weaknesses and BOLA/IDOR risks, and in this context, it can identify whether login endpoints exhibit inconsistent behavior that could facilitate credential guessing.

Additionally, if the API exposes verbose error messages (e.g., “user not found” vs “invalid password”), an attacker gains valuable feedback. DynamoDB conditional writes and uniqueness constraints on usernames can also affect how efficiently an attacker can enumerate valid users if the registration or username update paths leak existence through errors or timing.

Using middleBrick’s scan, which runs 12 security checks in parallel including Authentication, Rate Limiting, and Input Validation, you can detect whether your Fiber endpoints handling DynamoDB exhibit timing leaks, missing rate limits, or inconsistent error handling that could be leveraged in password spraying attacks.

To mitigate password spraying risks in a Fiber application backed by DynamoDB, standardize responses and enforce rate limiting at the application and infrastructure level. Below are concrete code examples using the AWS SDK for Go with DynamoDB.

First, ensure that your login handler returns a generic response regardless of whether the user exists. Use a constant-time comparison where possible and avoid branching on user existence.

// loginHandler processes a login request against DynamoDB
func loginHandler(c *fiber.Ctx) error {
    var req struct {
        Username string `json:"username"`
        Password string `json:"password"`
    }
    if err := c.BodyParser(&req); err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"message": "invalid request"})
    }

    // Fetch user record; handle conditional check for existence uniformly
    out, err := db.GetItem(&dynamodb.GetItemInput{
        TableName: aws.String("Users"),
        Key: map[string]types.AttributeValue{
            "PK": &types.AttributeValueMemberS{Value: "USER#" + req.Username},
        },
    })
    if err != nil {
        // Log the error internally, but return a generic response
        return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "invalid credentials"})
    }
    if out.Item == nil {
        // User does not exist; still perform a dummy hash operation to keep timing consistent
        dummyHash := hashPassword(req.Password) // dummy call, result not used
        _ = dummyHash
        return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "invalid credentials"})
    }

    // Compare hashed password; use a constant-time comparison function
    stored := aws.ToString(out.Item["PasswordHash"])
    if !compareHash(stored, req.Password) {
        return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "invalid credentials"})
    }

    // Issue session token or cookie
    return c.JSON(fiber.Map{"token": generateSessionToken(req.Username)})
}

// compareHash is a placeholder for a constant-time comparison
func compareHash(stored, input string) bool {
    // In production, use subtle.ConstantTimeCompare on byte representations
    return stored == input // simplified for example
}

// hashPassword is a placeholder for a proper key-derivation function
func hashPassword(password string) string {
    // Use bcrypt, scrypt, or argon2 in real code
    return password // simplified for example
}

Second, enforce rate limiting to restrict the number of login attempts per identity or IP. Use middleware to track attempts in DynamoDB with conditional writes to avoid race conditions.

// rateLimitMiddleware checks login attempts in DynamoDB
func rateLimitMiddleware(next fiber.Handler) fiber.Handler {
    return func(c *fiber.Ctx) error {
        username := string(c.FormValue("username"))
        if username == "" {
            return c.Next()
        }

        key := "RATE#" + username
        now := time.Now().Unix()
        // Attempt to record this attempt; if the item doesn't exist, create it
        input := &dynamodb.PutItemInput{
            TableName: aws.String("RateLimits"),
            Item: map[string]types.AttributeValue{
                "PK": &types.AttributeValueMemberS{Value: key},
                "Attempts": &types.AttributeValueMemberN{Value: aws.String("1")},
                "ExpiresAt": &types.AttributeValueMemberN{Value: aws.String(strconv.FormatInt(now+60, 10))},
            },
            ConditionExpression: aws.String("attribute_not_exists(PK)"),
        }
        _, err := db.PutItem(input)
        if err != nil {
            // If item exists, update atomically
            updateInput := &dynamodb.UpdateItemInput{
                TableName: aws.String("RateLimits"),
                Key: map[string]types.AttributeValue{
                    "PK": &types.AttributeValueMemberS{Value: key},
                },
                UpdateExpression: aws.String("SET Attempts = Attempts + :inc, ExpiresAt = :exp"),
                ConditionExpression: aws.String("attribute_exists(PK) AND expiresAt > :now"),
                ExpressionAttributeValues: map[string]types.AttributeValue{
                    ":inc": &types.AttributeValueMemberN{Value: aws.String("1")},
                    ":exp": &types.AttributeValueMemberN{Value: aws.String(strconv.FormatInt(now+60, 10))},
                    ":now": &types.AttributeValueMemberN{Value: aws.String(strconv.FormatInt(now, 10))},
                },
            }
            _, err = db.UpdateItem(updateInput)
            if err != nil {
                // Log and allow request to proceed or block based on policy
                return c.Next()
            }
        }

        // Check if attempts exceed threshold
        getInput := &dynamodb.GetItemInput{
            TableName: aws.String("RateLimits"),
            Key: map[string]types.AttributeValue{
                "PK": &types.AttributeValueMemberS{Value: key},
            },
        }
        out, _ := db.GetItem(getInput)
        if out.Item != nil {
            if val, ok := out.Item["Attempts"].(*types.AttributeValueMemberN); ok {
                count, _ := strconv.Atoi(*val.Value)
                if count > 10 {
                    return c.Status(fiber.StatusTooManyRequests).JSON(fiber.Map{"message": "too many attempts"})
                }
            }
        }
        return next(c)
    }
}

Third, apply global rate limiting at the infrastructure or gateway level to complement application controls. Configure timeouts and concurrency limits on your Fiber server to reduce the impact of sustained campaigns. middleBrick’s scan will highlight whether authentication endpoints lack rate limiting and whether error messages differ based on user existence, guiding you toward consistent, resilient designs.