Nosql Injection in Chi with Dynamodb

Chi is a minimal, idiomatic HTTP router for Go that relies on pattern matching and route parameters. When route parameters or query values are passed unchecked into a DynamoDB request, the input can alter the structure of the query. DynamoDB expressions use placeholder names and values; if user input is concatenated into expression attribute names or values without validation, the intended lookup can be transformed into a broader scan or an unintended condition.

Consider a profile lookup endpoint that accepts a user ID via a URL path parameter. In Chi, you might extract the parameter with chi.URLParam(r, "user_id"). If this value is directly interpolated into a DynamoDB GetItem or Query input, and the application builds expression strings by string concatenation, an attacker can supply values such as 1" OR begins_with(#status, ") to change the logical structure of the expression. Because DynamoDB does not support traditional SQL-style injection syntax, the impact manifests as condition bypass or unintended attribute filtering rather than classic database injection.

DynamoDB’s SDK expects expression attribute names and values to be provided separately. However, if an application dynamically builds the KeyConditionExpression or FilterExpression using string formatting with user input, the boundaries between expression structure and data can blur. For example, using Go’s fmt.Sprintf to construct expressions from URL parameters can allow an attacker to inject additional clauses, effectively widening the scan or changing the filter criteria. This violates the principle of unauthenticated attack surface exposure: the endpoint may return data belonging to other users or expose metadata that should remain private.

Another variant involves attribute-level access control implemented via expressions. If the application builds expressions that reference user-controlled attribute names, an attacker might supply names like username"; # to comment out part of the expression or to inject additional logic. Because DynamoDB supports nested structures and attribute sets, malformed input can change which attributes are evaluated or returned. This can lead to over-permissive results, effectively bypassing intended authorization checks that should be enforced at the query level.

Because middleBrick scans the unauthenticated attack surface and tests input validation across endpoints, it can detect cases where DynamoDB queries are constructed from untrusted input. Findings may highlight missing validation on route parameters, query strings, or headers that feed into expression building. The scanner does not fix the code; it provides findings with severity ratings and remediation guidance so developers can review how expressions are assembled and ensure placeholders are bound safely.

Secure DynamoDB usage in Chi centers on strict separation between expression structure and data. Always use the SDK’s built-in placeholder mechanisms and never concatenate user input into expression strings. Define attribute names as placeholder tokens (prefixed with #) and pass them via ExpressionAttributeNames. Define values as placeholders (prefixed with :) and pass them via ExpressionAttributeValues. This ensures user input is treated strictly as data, not as part of the expression syntax.

Below is a safe pattern for a Chi route that retrieves a user profile by ID and optional status filter. The example uses the AWS SDK for Go v2 (github.com/aws/aws-sdk-go-v2/service/dynamodb) and follows best practices for expression construction.

import (
    "context"
    "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"
)

func getUserProfile(router chi.Router, client *dynamodb.Client) {
    router.Get("/profiles/{user_id}", func(w http.ResponseWriter, r *http.Request) {
        userID := chi.URLParam(r, "user_id")
        status := r.URL.Query().Get("status")

        key := map[string]types.AttributeValue{
            "user_id": &types.AttributeValueMemberS{Value: userID},
        }

        input := &dynamodb.GetItemInput{
            TableName:                 aws.String("Profiles"),
            Key:                       key,
            ExpressionAttributeNames: map[string]string{
                "#pk": "user_id",
            },
            ExpressionAttributeValues: map[string]types.AttributeValue{
                ":uid": &types.AttributeValueMemberS{Value: userID},
            },
            KeyConditionExpression: aws.String("#pk = :uid"),
        }

        // If filtering by status is required and validated:
        if status != "" {
            input.FilterExpression = aws.String("#st = :statusVal")
            input.ExpressionAttributeNames["#st"] = "status"
            input.ExpressionAttributeValues[":statusVal"] = &types.AttributeValueMemberS{Value: status}
        }

        _, err := client.GetItem(r.Context(), input)
        if err != nil {
            http.Error(w, "Unable to retrieve profile", http.StatusInternalServerError)
            return
        }
        // handle response
    })
}

For query operations with dynamic conditions, avoid building the expression via string formatting. Instead, construct the expression stepwise and validate each token. For example, if you must support a prefix search on a string attribute, use the SDK’s built-in functions to ensure safe placeholder usage:

expr := "#name = :nameVal"
names := map[string]string{"#name": "username"}
vals := map[string]types.AttributeValue{
    ":nameVal": &types.AttributeValueMemberS{Value: "alice"},
}

if prefix != "" {
    expr = "#name begins_with :prefixVal"
    names["#name"] = "username"
    vals[":prefixVal"] = &types.AttributeValueMemberS{Value: prefix}
}

input := &dynamodb.QueryInput{
    TableName:                 aws.String("Users"),
    KeyConditionExpression:    aws.String(expr),
    ExpressionAttributeNames:  names,
    ExpressionAttributeValues: vals,
}

Additional remediation includes validating and whitelisting known values for status or prefix parameters, and applying length and pattern checks on incoming strings. middleBrick’s scans can surface endpoints where expressions are built from raw query parameters, enabling teams to locate and correct these patterns before deployment.