Spring4shell in Gin with Dynamodb

Spring4Shell (CVE-2022-22965) exploits a flaw in Spring MVC’s data binding when a class has both mutable properties and a binder-dependent setter (e.g., setData) that can be coerced into a map. In Go, a Gin-based service that accepts user input into a struct and then forwards values to an AWS DynamoDB client can mirror this class of vulnerability when reflection-based binding is used to populate parameters that are later passed to low-level DynamoDB operations.

Consider a Gin handler that binds JSON to a struct and then builds an AttributeValue map to send to DynamoDB. If the struct exposes a field that can be overridden via request parameters (e.g., nested objects or maps) and the handler trusts that input to construct condition expressions or attribute values, an attacker may inject unexpected keys that change control flow or data access patterns. For example, an attacker could provide a crafted JSON payload that sets a field used in a KeyConditionExpression to a malicious value, causing the query to target unintended items or to include additional attributes that expose sensitive data. The combination of Gin’s default binding flexibility and DynamoDB’s attribute-based access control can amplify the impact: unchecked user input can lead to BOLA/IDOR-like behavior when the constructed query uses attacker-controlled key conditions on a shared partition key.

Another vector involves serialization confusion. If the Gin service deserializes incoming data into an interface{} or map[string]interface{} before passing it to DynamoDB’s GetItem or Query, malformed attribute values (e.g., nested maps that mimic expected types) can bypass validation checks. Because DynamoDB strongly types attribute values (String, Number, Binary, etc.), mismatched types can cause the service to fall back to broader scans or conditional checks that expose more data than intended. This mirrors SSRF-adjacent risks where input influences which backend resources are accessed, but here the resource is a DynamoDB table whose permissions may be broader than intended.

To detect this pattern, a scanner such as middleBrick runs checks across Input Validation and BOLA/IDOR in parallel, correlating runtime behavior with the OpenAPI spec definitions for endpoints that interact with DynamoDB. It looks for endpoints that accept mutable structures and then use those structures to form low-level database requests without strict type validation or schema-bound filtering. The LLM/AI Security checks further probe whether prompt-style payloads can influence control logic, ensuring that even AI-assisted integrations remain bounded by expected data flows.

Remediation centers on strict input validation, schema-bound structs, and avoiding dynamic maps when constructing DynamoDB requests. Prefer explicit structs that mirror the expected DynamoDB attribute names and use conditional validation before calling the database. Never directly bind user input into map-based attribute builders used for GetItem or Query.

Example: Safe DynamoDB GetItem in Gin

Define a request DTO that only allows expected fields, and map to a strongly typed DynamoDB expression. This prevents unexpected keys from reaching the attribute builder.

// Request DTO: only allowed fields
 type QueryRequest struct {
     UserID string `json:"userId" validate:"required"`
     ItemID string `json:"itemId" validate:"required"`
 }

// Validate and map to DynamoDB GetInput
 func getItemInput(r QueryRequest) (*dynamodb.GetItemInput, error) {
     key := map[string]types.AttributeValue{
         "PK": &types.AttributeValueMemberS{Value: r.UserID},
         "SK": &types.AttributeValueMemberS{Value: r.ItemID},
     }
     return &dynamodb.GetItemInput{
         TableName: aws.String("MyTable"),
         Key:       key,
     }, nil
 }

// Gin handler
 func getItem(c *gin.Context) {
     var req QueryRequest
     if err := c.ShouldBindJSON(&req); err != nil {
         c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
         return
     }
     if err := validator.New().Struct(req); err != nil {
         c.JSON(http.StatusBadRequest, gin.H{"error": "invalid request"})
         return
     }
     input, err := getItemInput(req)
     if err != nil {
         c.JSON(http.StatusInternalServerError, gin.H{"error": "internal error"})
         return
     }
     out, err := dbClient.GetItem(c.Request.Context(), input)
     if err != nil {
         c.JSON(http.StatusInternalServerError, gin.H{"error": "database error"})
         return
     }
     c.JSON(http.StatusOK, out.Item)
 }

Example: Safe Query with Filter Expression

Use expression attribute values instead of injecting raw user strings into the key condition. This prevents type confusion and injection into the expression itself.

func queryItems(input QueryRequest) (*dynamodb.QueryOutput, error) {
     keyCondition := "PK = :uid"
     exprAttrVals := map[string]types.AttributeValue{
         ":uid": &types.AttributeValueMemberS{Value: input.UserID},
     }
     out, err := dbClient.Query(c.Request.Context(), &dynamodb.QueryInput{
         TableName:                 aws.String("MyTable"),
         KeyConditionExpression:    aws.String(keyCondition),
         ExpressionAttributeValues: exprAttrVals,
     })
     return out, err
 }

General Safeguards

• Use structured DTOs and do not expose map[string]interface{} for database construction.
• Validate required fields and types before constructing DynamoDB attribute values.
• Prefer condition expressions with bound attribute values over string concatenation.
• Audit IAM policies to ensure least privilege per table and operation.

These steps reduce the attack surface that an attacker could leverage when combining Gin routing behavior with DynamoDB’s attribute model, addressing both injection and authorization concerns.