Stack Overflow in Echo Go with Dynamodb
Stack Overflow in Echo Go with Dynamodb — how this specific combination creates or exposes the vulnerability
A common pattern in Go services using the Echo framework is to pass user-controlled identifiers directly into a DynamoDB request, for example via a URL path parameter such as user_id. If the service constructs a GetItem or Query input without validating or sanitizing that identifier, and then uses the result to build a response, it can set the conditions for a stack overflow–style abuse when the data size or nesting grows unexpectedly. In this specific combination, Echo’s routing and middleware chaining can cause a handler to be invoked multiple times or with large payloads when DynamoDB returns unexpectedly large items or paginated results that are not properly bounded.
Consider an Echo route that retrieves a user profile and then traverses related data stored in DynamoDB. If the handler iterates over a large nested attribute (such as a list of activity records) and recursively processes each entry without depth or size limits, a malicious actor could cause excessive memory use or stack exhaustion by storing deeply nested or very large items in DynamoDB. This is not a classic remote code execution flaw, but rather an application-layer stack overflow condition caused by unbounded processing of attacker-influenced data returned from the database.
Additionally, misconfigured pagination or scanning operations can exacerbate the issue. For instance, a Query with a filter that matches many items and a large page size can return substantial data in a single response, which Echo then processes in a handler that uses recursive functions. The combination of Echo’s flexible handler chaining and DynamoDB’s capacity to store large, complex items creates a scenario where an unauthenticated or low-privilege caller can trigger resource exhaustion by leveraging the database as a source of oversized payloads.
Another vector arises from DynamoDB Streams or event-driven integrations (e.g., via Lambda) feeding into an Echo service. If the service consumes stream records and recursively processes nested changes without validation, an attacker who can inject data into the stream (for example, through a compromised downstream system) can craft records that lead to stack overflow when Echo handlers interpret them. Because middleBrick tests the unauthenticated attack surface, such oversized-data paths can be detected as part of the Data Exposure and Input Validation checks, highlighting where size and structure limits are missing.
Dynamodb-Specific Remediation in Echo Go — concrete code fixes
To mitigate stack overflow risks when using DynamoDB with Echo, enforce strict size and depth limits on data read from the database before it is processed by handlers. Below are concrete, working examples in Go using the AWS SDK for Go v2.
Example 1: Safe GetItem with size validation
import (
"context"
"net/http"
"github.com/aws/aws-sdk-go-v2/aws"
"github.com/aws/aws-sdk-go-v2/config"
"github.com/aws/aws-sdk-go-v2/service/dynamodb"
"github.com/aws/aws-sdk-go-v2/service/dynamodb/types"
"github.com/labstack/echo/v4"
)
func GetProfile(c echo.Context) error {
userID := c.Param("user_id")
if userID == "" {
return echo.NewHTTPError(http.StatusBadRequest, "missing user_id")
}
cfg, err := config.LoadDefaultConfig(context.TODO())
if err != nil {
return echo.NewHTTPError(http.StatusInternalServerError, "unable to load AWS config")
}
client := dynamodb.NewFromConfig(cfg)
out, err := client.GetItem(context.TODO(), &dynamodb.GetItemInput{
TableName: aws.String("Users"),
Key: map[string]types.AttributeValue{
"user_id": &types.AttributeValueMemberS{Value: userID},
},
})
if err != nil {
return echo.NewHTTPError(http.StatusInternalServerError, "failed to get item")
}
if out.Item == nil {
return echo.NewHTTPError(http.StatusNotFound, "user not found")
}
// Enforce a maximum item size (e.g., 100 KB) to mitigate stack overflow risks
const maxItemSize = 100 * 1024
itemSize := estimateItemSize(out.Item)
if itemSize > maxItemSize {
return echo.NewHTTPError(http.StatusBadRequest, "profile data too large")
}
// Safe processing: iterate with depth limits
if err := processItemLimited(out.Item, 0, 10); err != nil {
return echo.NewHTTPError(http.StatusBadRequest, err.Error())
}
return c.JSON(http.StatusOK, out.Item)
}
func estimateItemSize(item map[string]types.AttributeValue) int {
// Simplified size estimation; in production, use a robust encoder or AWS tooling
// This is illustrative and not exact.
size := 0
for _, v := range item {
size += len(v.String())
}
return size
}
func processItemLimited(item map[string]types.AttributeValue, depth, maxDepth int) error {
if depth > maxDepth {
return echo.NewHTTPError(http.StatusBadRequest, "data nesting too deep")
}
for _, v := range item {
if m, ok := v.(types.AttributeValue).(map[string]types.AttributeValue); ok {
if err := processItemLimited(m, depth+1, maxDepth); err != nil {
return err
}
}
}
return nil
}
Example 2: Query with pagination and result-size guard
func ListItems(c echo.Context) error {
cfg, err := config.LoadDefaultConfig(context.TODO())
if err != nil {
return echo.NewHTTPError(http.StatusInternalServerError, "unable to load AWS config")
}
client := dynamodb.NewFromConfig(cfg)
input := &dynamodb.QueryInput{
TableName: aws.String("Items"),
KeyConditionExpression: aws.String("owner = :owner"),
ExpressionAttributeValues: map[string]types.AttributeValue{
":owner": &types.AttributeValueMemberS{Value: "alice"},
},
Limit: aws.Int32(100), // Enforce server-side limit
}
var totalSize int
const maxTotalSize = 500 * 1024 // 500 KB cap across pages
for {
out, err := client.Query(context.TODO(), input)
if err != nil {
return echo.NewHTTPError(http.StatusInternalServerError, "query failed")
}
for _, item := range out.Items {
totalSize += estimateItemSize(item)
if totalSize > maxTotalSize {
return echo.NewHTTPError(http.StatusBadRequest, "query result too large")
}
if err := processItemLimited(item, 0, 8); err != nil {
return err
}
}
if out.LastEvaluatedKey == nil {
break
}
input.ExclusiveStartKey = out.LastEvaluatedKey
}
return c.JSON(http.StatusOK, "ok")
}
These examples demonstrate concrete remediation: validate item sizes on read, limit recursion depth, and enforce server-side pagination caps. By combining these patterns with Echo’s structured error handling, you reduce the attack surface that an unauthenticated or low-privilege user can leverage via DynamoDB data. middleBrick can help surface such risks by scanning your endpoints and flagging missing size and depth controls as part of its Input Validation and Data Exposure checks.