Type Confusion in Fiber with Dynamodb

Type confusion in a Fiber application that interacts with DynamoDB typically arises when application code deserializes user-controlled JSON into Go structs with mismatched or overly flexible types, and then passes values directly to DynamoDB operations. Because DynamoDB stores data as loosely typed attribute-value pairs (e.g., strings, numbers, booleans, nested maps, and lists), Go code that uses interface{} or type-agnostic maps can inadvertently accept values that change type between requests. If the application logic assumes a consistent concrete type (e.g., expecting a string for userID or an int64 for quantity) without validating or asserting the underlying type, an attacker can supply a different type (e.g., a number where a string is expected, or a nested map instead of a scalar) and cause misbehavior, such as bypassing authorization checks or triggering unexpected comparisons.

In a Fiber route, this often occurs when binding incoming JSON to a struct or map and then using that data in a DynamoDB expression attribute values map. Consider a handler that updates an item using a user-supplied ID and a numeric version for optimistic locking. If the version field is not strictly typed, an attacker could send a string, causing the conditional expression to behave differently or skip the condition entirely. Because DynamoDB does not enforce a rigid schema at write time, the server-side code must enforce types; otherwise, the combination of Fiber’s flexible routing and DynamoDB’s schemaless nature creates a surface where type confusion can lead to logic flaws such as IDOR (BOLA) or incorrect state transitions.

Additionally, nested structures can amplify the risk. A DynamoDB item may contain a map attribute that the Go code unmarshals into map[string]interface{}. If downstream logic iterates over this map and uses type switches or assertions without exhaustive checks, an attacker who can influence the map’s value types may force execution paths that treat an array as a scalar, or a boolean as a string, leading to incorrect authorization or data handling. This is especially relevant when using the AWS SDK for Go v2 with DynamoDB’s attribute value protocol, where raw JSON from the client is mapped into attribute values. Without strict validation and type assertions, the application may misinterpret data, confuse numeric IDs with pointers, or mishandle conditional updates, effectively exposing the attack surface that middleBrick’s BOLA/IDOR and Input Validation checks are designed to surface.

To remediate type confusion in Fiber when working with DynamoDB, enforce strict types at binding and before constructing DynamoDB expressions. Prefer binding to strongly typed structs, validate required fields, and avoid using interface{} unless absolutely necessary. When you must use interface{}, apply explicit type assertions with a fallback and log anomalies. Below are concrete, working examples using the AWS SDK for Go v2 with DynamoDB and Fiber.

Example 1: Strongly typed struct with validation

// Define a strict input struct
type UpdateItemInput struct {
    UserID  string `json:"userId" validate:"required,uuid"`
    Version int64  `json:"version" validate:"required,min=0"`
    Status  string `json:"status" validate:"required,oneof=pending active archived"`
}

// Handler
app.Put("/items/:id", func(c *fiber.Ctx) error {
    var in UpdateItemInput
    if err := c.BodyParser(&in); err != nil {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": "invalid body"})
    }
    // Validate using a validator (e.g., github.com/go-playground/validator)
    if valid, err := validateStruct(in); err != nil || !valid {
        return c.Status(fiber.StatusBadRequest).JSON(fiber.Map{"error": err.Error()})
    }

    // Build DynamoDB expression with typed attribute values
    expr, err := expression.NewBuilder().
        WithCondition(expression.Name("version").Equal(expression.Value(in.Version))).
        Build()
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": "failed to build expression"})
    }

    av, err := dynamodbattribute.MarshalMap(map[string]interface{}{
        "status": in.Status,
        "version": in.Version,
    })
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": "failed to marshal"})
    }

    _, err = svc.UpdateItem(c.Context(), &dynamodb.UpdateItemInput{
        TableName: aws.String("Items"),
        Key: map[string]AttributeValue{
            "user_id": &AttributeValueMemberS{Value: in.UserID},
        },
        UpdateExpression:          expr.Update(),
        ExpressionAttributeNames:  expr.Names(),
        ExpressionAttributeValues: av,
        ConditionExpression:       expr.Condition(),
    })
    if err != nil {
        return c.Status(fiber.StatusInternalServerError).JSON(fiber.Map{"error": err.Error()})
    }
    return c.SendStatus(fiber.StatusOK)
})

Example 2: Safe handling of interface{} when schema is dynamic

// When you must accept a map, validate and assert explicitly
func safeUpdate(data map[string]interface{}) error {
    userID, ok := data["userId"].(string)
    if !ok || userID == "" {
        return errors.New("userId must be a non-empty string")
    }
    version, ok := data["version"].(float64) // JSON numbers decode as float64
    if !ok {
        return errors.New("version must be a number")
    }
    status, ok := data["status"].(string)
    if !ok || status == "" {
        return errors.New("status must be a non-empty string")
    }

    av, err := dynamodbattribute.MarshalMap(map[string]interface{}{
        "user_id": userID,
        "version": int64(version),
        "status":  status,
    })
    if err != nil {
        return err
    }

    // Use av in DynamoDB call as shown in Example 1
    return nil
}

These patterns ensure that types are validated before they reach DynamoDB expressions, reducing the risk of type confusion. For ongoing assurance, integrate the middleBrick CLI to scan endpoints with middlebrick scan <url>, review findings in the Web Dashboard, or add the GitHub Action to fail builds when risk scores degrade. In proactive environments, the Pro plan enables continuous monitoring and CI/CD pipeline gates, while the MCP Server lets you scan APIs directly from your AI coding assistant during development.