Cache Poisoning in Buffalo with Dynamodb

Cache poisoning in a Buffalo application that uses Amazon DynamoDB as a backend data store occurs when an attacker causes cached responses to store malicious or incorrect data that later gets served to other users. This typically leverages weak input validation or unsafe caching behavior where the cache key does not sufficiently differentiate between attacker-controlled and legitimate inputs.

Buffalo applications often rely on middleware or in-memory caches to reduce repeated calls to DynamoDB. If the cache key incorporates user-supplied values—such as query parameters, headers, or path segments—and those values are not strictly validated and normalized, an attacker can craft requests that result in cache entries being written under keys that other users’ requests subsequently match. For example, an endpoint like /products/{id} that caches responses based on the raw id parameter could be abused if id is not canonicalized or validated against the actual DynamoDB primary key structure.

DynamoDB itself does not introduce cache poisoning, but its usage patterns can amplify the risk if the application layer misuses cached responses. Consider a scenario where a Buffalo handler performs a GetItem using a user-provided ID, stores the raw response in a cache, and later reuses that response for different IDs due to a flawed cache key design. This can lead to one user seeing another user’s data, or an attacker forcing the cache to store specially crafted items that cause downstream logic to behave unexpectedly.

Additionally, if the application caches error responses or partial results—such as when a DynamoDB operation fails and the error is cached for identical-looking requests—an attacker may be able to poison the cache with misleading error states that affect availability or integrity. The interplay between Buffalo’s request handling lifecycle and DynamoDB’s key-based access patterns means developers must ensure cache keys are deterministic, scoped correctly, and tightly bound to the authenticated user context and the precise query or mutation being performed.

To mitigate cache poisoning when using DynamoDB with Buffalo, focus on strict input validation, canonical cache keys, and isolating cache entries by user context. Below are concrete code examples that demonstrate safe patterns.

• Validate and sanitize IDs before database calls: Ensure that IDs conform to expected patterns and map correctly to DynamoDB key schemas before using them in queries or cache keys.
• Use deterministic, scoped cache keys: Include user identifiers and operation specifics in cache keys to prevent cross-user cache pollution.
• Do not cache sensitive or user-specific responses in shared caches: If caching is necessary, scope entries to the authenticated user and avoid caching write-triggered or error responses.

Example: Safe DynamoDB GetItem with validated ID and user-scoped cache key

// handlers/products.go
package handlers

import (
    "context"
    "fmt"
    "net/http"
    "regexp"

    "github.com/gobuffalo/buffalo"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb"
    "github.com/aws/aws-sdk-go-v2/service/dynamodb/types"
)

var idRegex = regexp.MustCompile(`^[A-Za-z0-9_-]{1,64}$`)

func GetProduct(c buffalo.Context) error {
    pid := c.Param("id")
    // Validate ID format strictly
    if !idRegex.MatchString(pid) {
        return c.Error(http.StatusBadRequest, fmt.Errorf("invalid product ID"))
    }

    // Build a user-specific cache key to avoid cross-user poisoning
    userID := c.Session().Get("user_id")
    cacheKey := fmt.Sprintf("product:%v:user:%v", pid, userID)

    // Pseudocode cache operations (replace with your actual cache implementation)
    if cached, ok := cache.Get(cacheKey); ok {
        c.Set("product", cached)
        return c.Render(http.StatusOK, r.JSON(cached))
    }

    // Perform DynamoDB GetItem
    svc := dynamodb.NewFromConfig(cfg)
    out, err := svc.GetItem(c, &dynamodb.GetItemInput{
        TableName: aws.String("Products"),
        Key: map[string]types.AttributeValue{
            "ID": &types.AttributeValueMemberS{Value: pid},
        },
    })
    if err != nil {
        return c.Error(http.StatusInternalServerError, err)
    }
    if out.Item == nil {
        return c.Render(http.StatusNotFound, r.JSON(nil))
    }

    // Convert DynamoDB attribute map to a plain structure as needed
    product := convertItem(out.Item)

    // Store in user-scoped cache with appropriate TTL
    cache.Set(cacheKey, product, 300)

    return c.Render(http.StatusOK, r.JSON(product))
}

Example: Cache error responses only when safe and scoped

// handlers/search.go
package handlers

func Search(c buffalo.Context) error {
    query := c.Param("q")
    if query == "" {
        return c.Error(http.StatusBadRequest, fmt.Errorf("query is required"))
    }

    userID := c.Session().Get("user_id")
    cacheKey := fmt.Sprintf("search:q=%v:user=%v", query, userID)

    if cached, ok := cache.Get(cacheKey); ok {
        return c.Render(200, r.JSON(cached))
    }

    // Execute DynamoDB query (pseudocode)
    results, err := executeDynamoDBSearch(c, query)
    if err != nil {
        // Do not cache error responses globally; return directly
        return c.Error(500, err)
    }

    cache.Set(cacheKey, results, 60)
    return c.Render(200, r.JSON(results))
}

These patterns ensure that cache keys incorporate user context and validated input, reducing the risk of cache poisoning. In combination with the middleBrick scanner—which can detect cache poisoning risks in your API surface—you can validate that your caching logic does not inadvertently allow cross-user data exposure or malicious cache injection.