Heap Overflow in Chi with Dynamodb

A heap overflow in a Chi application that interacts with DynamoDB typically arises when unbounded or untrusted input is used to allocate memory for structures that are later serialized, iterated, or passed to low-level operations. In Chi, route parameters, query strings, and JSON payloads are bound into typed structures; if these structures contain slices or strings that grow based on attacker-controlled values without proper length checks, the underlying memory can expand beyond safe limits. When the same data is used to build DynamoDB expressions or batch operations, oversized attribute values or unexpected nesting can trigger excessive memory usage during marshaling, condition building, or result deserialization.

DynamoDB amplifies the exposure because it encourages storing and retrieving large or deeply nested documents. If a Chi handler deserializes a DynamoDB record into a Go slice or map without validating element counts or string lengths, an attacker can supply a crafted payload that causes the Chi application to allocate far more memory than expected. This can degrade performance or lead to crashes, and it compounds risks when combined with other unchecked inputs in the request lifecycle. Although Go’s runtime provides some protection against classic C-style heap overflows, logical overflows—where allocated sizes exceed intended limits—remain a concern, especially when constructing dynamic expressions for DynamoDB’s condition builders or when using the SDK’s attribute value marshaling.

An example scenario: a Chi route accepts a JSON filter to query DynamoDB, and the filter’s attribute value list is bound directly into a slice without length validation. A malicious request with thousands of IDs causes the handler to build a large AttributeValue batch, increasing memory pressure and potentially leading to denial of service. MiddleBrick scans can surface such input validation and unsafe consumption findings by correlating runtime behavior with OpenAPI specs, highlighting where untrusted data reaches DynamoDB-related operations without adequate safeguards.

Remediation focuses on validating and bounding inputs before they influence DynamoDB interactions and memory allocation. In Chi, use structured binding with explicit limits, enforce size constraints on strings and slices, and avoid directly forwarding user data into DynamoDB expression builders.

import (
    "net/http"
    "github.com/go-chi/chi/v5"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb/types"
    "github.com/aws/smithy-go/middleware"
)

// Safe: bounded binding and validation before DynamoDB usage
type FilterRequest struct {
    IDs []string `json:"ids" validate:">=1,le=50"` // enforce max 50 IDs
}

func SafeQueryHandler(db *dynamodb.Client) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        var req FilterRequest
        if err := binding.Bind(r, &req); err != nil {
            http.Error(w, "invalid request", http.StatusBadRequest)
            return
        }
        // Validate each ID length to avoid oversized attribute values
        for _, id := range req.IDs {
            if len(id) == 0 || len(id) > 256 {
                http.Error(w, "invalid id length", http.StatusBadRequest)
                return
            }
        }
        // Build expression safely with bounded placeholders
        var ids []types.AttributeValue
        for _, id := range req.IDs {
            ids = append(ids, &types.AttributeValueMemberS{Value: id})
        }
        // Use a limited batch get or query instead of unbounded condition expressions
        // Example: batchGetItem with up to 100 keys per request
        // (pseudo-code; implement pagination and chunking in production)
        _, err := db.BatchGetItem(r.Context(), &dynamodb.BatchGetItemInput{
            RequestItems: map[string]types.KeysAndAttributes{
                "my-table": {
                    Keys: ids,
                },
            },
        })
        if err != nil {
            http.Error(w, "failed to query", http.StatusInternalServerError)
            return
        }
        w.WriteHeader(http.StatusOK)
    }
}

Key practices: enforce maximum lengths on strings and slices, avoid unbounded loops when constructing AttributeValue lists, and prefer chunked operations (e.g., batch reads) over expansive condition expressions. Do not rely on client-supplied field counts or nested depths; validate them explicitly. MiddleBrick’s checks for input validation and unsafe consumption can help identify missing bounds in routes that generate DynamoDB requests, while its inventory management and property authorization checks ensure that sensitive attributes are not over-fetched or exposed.

For continuous protection, use the middleBrick CLI to scan from terminal with middlebrick scan <url>, add the GitHub Action to fail builds if risk scores drop below your threshold, or run scans via the MCP server inside your AI coding assistant to catch issues early during development.