Server Side Template Injection in Gin with Dynamodb

Server Side Template Injection (SSTI) in a Gin application that interacts with DynamoDB can occur when user-controlled input is passed into a template execution path and the data is later used to construct DynamoDB API calls. While SSTI is commonly associated with server-side templating engines (e.g., Go’s html/template, text/template), the risk in Gin arises when developers inadvertently pass raw user input into template functions or context objects that later influence DynamoDB expression attribute values or condition expressions. For example, if a Gin handler binds a request query parameter to a struct and then passes that struct to a template for rendering, and the same struct is used to build a GetItem input map, an attacker may attempt to inject template directives that alter how the data is interpreted when constructing the request to DynamoDB.

In this specific combination, the vulnerability surface is not in DynamoDB itself, which is a NoSQL database without native template processing, but in how the application layer builds requests. If user input flows from an SSTI-affected template context into code that builds DynamoDB ExpressionAttributeValues or uses string concatenation to form KeyConditionExpression, unsafe reflection or map access patterns may expose sensitive data or enable privilege escalation. For instance, an attacker might supply a payload designed to traverse object properties in the template engine, reaching fields that are later used as keys or filters in DynamoDB operations. This can lead to unintended data exposure or authorization bypass when access control checks are incomplete before constructing the request.

Consider a Gin handler that uses a template to render a user profile page and also uses the same user identifier to fetch data from DynamoDB. If the identifier is sourced from user input and not validated, and the template execution context allows property access, an attacker could attempt path traversal via template syntax to reach other struct fields. Those fields might include a DynamoDB key attribute that is later used without additional authorization checks. The scanner checks in middleBrick’s 12 parallel security checks will flag such patterns under Authentication, BOLA/IDOR, and Unsafe Consumption, highlighting that data used in DynamoDB requests originates from unvalidated template contexts.

An example of risky code would involve binding a query parameter to a map that is both rendered by a template and used as input for DynamoDB:

input := make(map[string]interface{})
if err := c.BindQuery(&input); err != nil {
    c.JSON(400, gin.H{"error": "invalid query"})
    return
}
// Unsafe: using user-influenced map directly in template
if err := c.HTML(200, "profile.tmpl", input); err != nil {
    c.JSON(500, gin.H{"error": "render error"})
}
// Unsafe: same map used to build DynamoDB input without sanitization
dynamoInput := map[string]*dynamodb.AttributeValue{
    "PK": {S: input["id"].(*string)},
}
_, err := svc.GetItem(context.TODO(), &dynamodb.GetItemInput{
    TableName: aws.String("Users"),
    Key:       dynamoInput,
})

While DynamoDB does not process templates, the insecure flow of data from template context to database request can expose sensitive operations or enable bypasses. middleBrick detects such patterns by correlating template injection findings with DynamoDB usage in the runtime scan, emphasizing the need to isolate user input from request construction.

To remediate SSTI-related risks when working with DynamoDB in Gin, ensure strict separation between user input used for rendering and input used for database operations. Validate and sanitize all user-supplied data before it enters any template context, and avoid reusing the same data structures for both presentation and persistence without explicit transformation. Apply allowlists for expected values, especially for keys used in DynamoDB requests, and use structured types instead of raw maps.

Prefer strongly typed structs for Gin binding and explicitly map only the required fields to DynamoDB expression attributes. This prevents unintended property traversal in templates and ensures that only intended fields are used in database requests. Below is a secure pattern using a dedicated request struct and explicit mapping:

type GetUserRequest struct {
    UserID string `form:"user_id" binding:"required,alphanum"`
}

type UserProfile struct {
    UserID string
    Name   string
    Email  string
}

func GetUserHandler(svc *dynamodb.Client) gin.HandlerFunc {
    return func(c *gin.Context) {
        var req GetUserRequest
        if err := c.ShouldBindQuery(&req); err != nil {
            c.JSON(400, gin.H{"error": "invalid parameters"})
            return
        }

        // Validate format further if needed, e.g., UUID check

        // Fetch from DynamoDB using a controlled key
        input := &dynamodb.GetItemInput{
            TableName: aws.String("Users"),
            Key: map[string]*dynamodb.AttributeValue{
                "PK": {S: aws.String("USER#" + req.UserID)},
            },
        }
        result, err := svc.GetItem(c.Request.Context(), input)
        if err != nil {
            c.JSON(500, gin.H{"error": "unable to fetch user"})
            return
        }
        if result.Item == nil {
            c.JSON(404, gin.H{"error": "user not found"})
            return
        }

        // Map to a safe profile struct for rendering
        profile := UserProfile{
            UserID: *result.Item["PK"].S,
            Name:   *result.Item["Name"].S,
            Email:  *result.Item["Email"].S,
        }

        // Render with a restricted context, not the raw request map
        c.HTML(200, "profile.tmpl", gin.H{
            "Profile": profile,
        })
    }
}

Additionally, configure your templates to disallow or escape any dynamic property access that could enable traversal. In production, enforce that templates operate only on explicitly passed data structures and never on request-bound maps that may contain raw user input. middleBrick’s scans will highlight remaining risks in areas like Authentication, BOLA/IDOR, and Unsafe Consumption, guiding you toward stricter input validation and least-privilege data usage.

For continuous assurance, use the middleBrick CLI to scan endpoints during development: middlebrick scan https://api.example.com, or integrate the GitHub Action to fail builds if security scores drop below your chosen threshold. The Pro plan supports continuous monitoring and CI/CD integration for recurring verification as your API evolves.