Cache Poisoning in Gorilla Mux with Dynamodb

Cache poisoning in a Gorilla Mux routing context occurs when an attacker manipulates cache keys or cacheable responses so that malicious or incorrect data is served to subsequent users. When Gorilla Mux is used to route requests to backend services that interact with DynamoDB, several characteristics of the routing layer and the database access patterns can unintentionally create or expose cache poisoning risks.

Gorilla Mux matches incoming HTTP requests using path variables, query parameters, and headers to decide which handler to invoke. If route definitions use per-user or per-request variables (for example, a path like /users/{userID}/profile) but responses are cached with a key that does not include the full set of user-specific variables, one user may receive another user’s data. This is a classic cache key collision that becomes more dangerous when the handler builds a DynamoDB query using values taken directly from the route without normalizing or validating them. For instance, if the handler extracts vars["userID"] and uses it as a partition key in a DynamoDB request, a response intended for user A could be cached under a key that user B’s request inadvertently matches, leading to unauthorized data exposure.

Query parameters also contribute to cache poisoning risk. Suppose a handler passes query parameters such as limit, sort, or filter to a DynamoDB scan or query without including them in the cache key. An attacker could craft a request that injects a malicious filter or sorting order, and if the response is cached, other users receive the attacker-influenced data. In DynamoDB, filters are applied after a read operation, so a poisoned query could cause the backend to retrieve a broader or different dataset than intended, and the cache would then serve that dataset to others.

DynamoDB’s attribute-based access patterns can amplify these issues when combined with improper caching. If a handler caches entire items or query result sets without normalizing attribute names or values, subtle differences such as case sensitivity in string keys or inconsistent timestamp formats can produce distinct cache entries that should have been unified. Additionally, if the handler uses conditional writes or updates based on cached values without revalidating the source of those values, an attacker who can poison the cache may indirectly influence write paths, leading to inconsistent state across items in DynamoDB.

Another vector involves HTTP headers that are not considered in cache key construction. Gorilla Mux can route based on headers such as authorization tokens or tenant identifiers, but if the caching layer ignores these headers, a cached response meant for one tenant or privilege level may be served to another. Because DynamoDB does not inherently understand HTTP semantics, the backend must enforce tenant and privilege boundaries in application logic; if caching bypasses these checks, the system effectively delegates authorization to the cache, which is unsafe.

To understand the risk in the context of security scans, tools such as middleBrick include checks for improper caching and authorization handling among its 12 parallel security checks. These scans test the unauthenticated attack surface and can surface findings related to data exposure and authorization bypass that may stem from cache poisoning issues in routing and database access patterns. While the scanner provides prioritized findings and remediation guidance, developers must address the root causes in code and architecture to prevent data leakage or integrity issues.

Remediation focuses on ensuring that cache keys incorporate all request dimensions that affect the response, validating and normalizing inputs used in DynamoDB queries, and enforcing data boundaries at the handler level before any caching occurs.

First, construct cache keys from the full set of variables that influence the response, including route parameters, query parameters, and relevant headers. In Gorilla Mux, you can build a deterministic key in your handler:

import (
    "fmt"
    "net/http"
    "github.com/gorilla/mux"
)

func cacheKey(r *http.Request) string {
    vars := mux.Vars(r)
    userID := vars["userID"]
    limit := r.URL.Query().Get("limit")
    sort := r.URL.Query().Get("sort")
    // Include a header that identifies tenant or scope if used for routing
    tenant := r.Header.Get("X-Tenant-ID")
    return fmt.Sprintf("user:%s:limit:%s:sort:%s:tenant:%s", userID, limit, sort, tenant)
}

Second, when querying DynamoDB, use strongly typed parameters and avoid concatenating raw user input into key expressions. Prefer condition expressions and prepared attribute names. The following example shows a safe DynamoDB query inside a Gorilla Mux handler:

import (
    "context"
    "github.com/aws/aws-sdk-go-v2/aws"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb/types"
    "github.com/aws/aws-sdk-go-v2/feature/dynamodb/attributevalue"
)

type Profile struct {
    UserID string `dynamodbav:"userID"`
    Email  string `dynamodbav:"email"`
}

func getProfileHandler(svc *dynamodb.Client) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        vars := mux.Vars(r)
        userID := vars["userID"]
        // Validate input before using it in a key expression
        if userID == "" {
            http.Error(w, "missing userID", http.StatusBadRequest)
            return
        }
        out, err := svc.GetItem(r.Context(), &dynamodb.GetItemInput{
            TableName: aws.String("Profiles"),
            Key: map[string]types.AttributeValue{
                "userID": &types.AttributeValueMemberS{Value: userID},
            },
        })
        if err != nil {
            http.Error(w, "failed to retrieve profile", http.StatusInternalServerError)
            return
        }
        var profile Profile
        if err := attributevalue.UnmarshalMap(out.Item, &profile); err != nil {
            http.Error(w, "failed to unmarshal item", http.StatusInternalServerError)
            return
        }
        // Only serve the cached or retrieved data after confirming ownership in application logic
        // (e.g., by comparing the authenticated user ID with profile.UserID)
        _ = profile
        // respond with profile
    }
}

Third, when caching query results that involve DynamoDB scans or queries with variable filters, include those filter values in the cache key and avoid caching results that contain sensitive or user-specific fields unless the cache is segmented by tenant or user. Consider normalizing values (e.g., lowercasing strings, trimming whitespace) before incorporating them into cache keys to reduce collisions caused by superficial differences.

Finally, combine these measures with runtime security scanning. middleBrick can be used to validate that your API endpoints properly handle authorization and caching by running its unauthenticated scans. The CLI tool makes this straightforward:

middlebrick scan https://api.example.com/profiles

Using the GitHub Action or MCP Server can further ensure that cache-related issues are caught before deployment or during development. These integrations do not replace code fixes but help maintain consistent security posture across environments.