Type Confusion in Echo Go with Dynamodb

Type confusion in an Echo Go service that uses DynamoDB typically arises when application code deserializes request data into loosely-typed structures and then passes values directly to DynamoDB operations without strict validation. In Go, this often occurs when using interface{} or map[string]interface{} to unmarshal JSON input, and then constructing DynamoDB attribute values (such as map[string]*dynamodb.AttributeValue) from that data without enforcing concrete types.

For example, an attacker may supply a JSON payload where a field expected to be a string is instead an object or a number. If the handler uses type assertions without checking the underlying type, the runtime may misinterpret the data shape. When that data is later used in condition expressions or key construction for DynamoDB (e.g., KeyConditionExpression or UpdateItem input), the mismatch can cause the application to behave unexpectedly, potentially bypassing intended access controls or query constraints.

Consider an endpoint that retrieves items by a user ID and a sort key derived from a numeric timestamp. If the timestamp is unmarshalled as a float64 due to JSON number handling, but later type-asserted to an int or used in a string-based key expression without conversion, the query may match unintended items or produce errors that expose internal logic. In a black-box scan, middleBrick tests inputs such as objects where strings are expected and observes whether the API response or behavior diverges, indicating type confusion. This maps to the Authentication and Input Validation checks in middleBrick, which look for unexpected input handling that can weaken authorization boundaries.

When combined with DynamoDB, type confusion can amplify risks around BOLA/IDOR and Property Authorization. If an attacker manipulates type assertions to inject unexpected values into key expressions or filter conditions, they might query or update items outside their intended scope. For instance, supplying a nested object where a simple string is expected could cause condition expressions to evaluate incorrectly, bypassing intended partition key equality checks. middleBrick’s OpenAPI/Swagger analysis cross-references spec definitions with runtime findings to highlight mismatches between declared types and observed input handling, emphasizing the importance of strict schema validation before constructing DynamoDB requests.

Real-world patterns include using json.RawMessage to delay deserialization or implementing custom unmarshal logic to enforce concrete types before building DynamoDB input structures. Defensive practices involve validating and converting each incoming field to the expected Go type (e.g., strconv.Atoi for numeric IDs, explicit string checks for partition keys) and avoiding interface{} propagation into DynamoDB attribute construction. By ensuring that data shapes are verified before they influence query construction, you reduce the chance that type confusion leads to authorization bypass or inconsistent behavior that an automated scanner can detect and report.

To remediate type confusion in Echo Go when working with DynamoDB, enforce strict types before constructing DynamoDB attribute values. Define concrete structs for request and key data, and validate each field before using it in queries or updates. Avoid relying on interface{} for user-supplied values that affect key expressions or condition logic.

Below is a secure example that parses a path parameter and a JSON body, validates types, and builds DynamoDB input safely:

// types
type ItemRequest struct {
    UserID string `json:"user_id"`
    Timestamp int64 `json:"timestamp"`
    Data string `json:"data"`
}

// handler
func getItemHandler(c echo.Context) error {
    userID := c.Param("user_id")
    if userID == "" {
        return echo.NewHTTPError(http.StatusBadRequest, "missing user_id")
    }

    var req ItemRequest
    if err := c.Bind(&req); err != nil {
        return echo.NewHTTPError(http.StatusBadRequest, "invalid payload")
    }
    if req.UserID != userID {
        return echo.NewHTTPError(http.StatusForbidden, "mismatch user_id")
    }

    // Build DynamoDB key with correct types
    key := map[string]*dynamodb.AttributeValue{
        "PK": {S: aws.String("USER#" + req.UserID)},
        "SK": {S: aws.String("TIMESTAMP#" + strconv.FormatInt(req.Timestamp, 10))},
    }

    input := &dynamodb.GetItemInput{
        TableName: aws.String("Items"),
        Key:       key,
    }

    // Use input with DynamoDB client...
    return c.JSON(http.StatusOK, map[string]string{"status": "ok"})
}

For update operations, validate and convert each field explicitly:

updateInput := &dynamodb.UpdateItemInput{
    TableName: aws.String("Items"),
    Key: map[string]*dynamodb.AttributeValue{
        "PK": {S: aws.String("USER#" + req.UserID)},
        "SK": {S: aws.String("TIMESTAMP#" + strconv.FormatInt(req.Timestamp, 10))},
    },
    UpdateExpression: aws.String("SET data = :d"),
    ExpressionAttributeValues: map[string]*dynamodb.AttributeValue{
        ":d": {S: aws.String(req.Data)},
    },
    ConditionExpression: aws.String("attribute_exists(PK) AND attribute_exists(SK)"),
}

By binding request data to concrete structs, converting numeric inputs with explicit parsing, and constructing DynamoDB attribute maps with typed values, you mitigate type confusion. This aligns with input validation checks in middleBrick and supports findings mapped to frameworks such as OWASP API Top 10 and PCI-DSS. If your project includes automated scans, the middleBrick CLI allows you to validate endpoints from the terminal:

middlebrick scan https://api.example.com/v1/items

For pipeline enforcement, the middleBrick GitHub Action can fail builds when risk scores drop below your configured threshold, and the Pro plan adds continuous monitoring to detect regressions in DynamoDB-related endpoints over time.