Cache Poisoning in Buffalo with Firestore

Cache poisoning in the Buffalo web framework when using Google Cloud Firestore as a backend can occur when dynamic query results derived from attacker-controlled input are stored in shared cache keys. Because Firestore documents often include user-specific or tenant-specific fields (such as owner IDs or organization slugs), caching a response keyed only on non-authoritative parts of the request (e.g., path or non-user headers) can cause one user’s data to be served to another. This typically happens when the cache key omits the authenticated subject or tenant context, enabling an authenticated attacker to manipulate the cache contents and observe or modify data belonging to other users.

Buffalo does not provide built-in cache stores; developers commonly plug in Redis or in-memory stores. If the application caches HTTP responses or query results based on raw request parameters without normalizing or scoping by the authenticated identity or Firestore document path, an attacker can craft requests that inject crafted query parameters or headers. These inputs influence Firestore queries that return sensitive documents, and the resulting response is cached under a key that does not include user or tenant context. Subsequent requests with different identities may inadvertently receive the poisoned cache entry, leading to information disclosure or inconsistent application behavior.

Firestore security rules can limit read access per document, but they do not protect shared cache layers outside of Firestore. If a Buffalo handler bypasses proper authorization checks before caching, or if the cache key does not incorporate the Firestore document path or user ID, an attacker may leverage cache poisoning to retrieve documents they should not see. Real-world attack patterns include tampering with query parameters like projectId or userId to change the Firestore query, then forcing the poisoned result into the cache. This maps to OWASP API Top 10:2023’s Broken Object Level Authorization (BOLA) and can expose PII or sensitive business data. MiddleBrick scans detect these authorization and data exposure risks by correlating Firestore query patterns with cached responses and highlighting missing tenant-aware cache scoping.

To remediate cache poisoning when using Buffalo with Firestore, ensure cache keys include tenant and user context, enforce Firestore authorization on every read, and avoid caching responses that contain user-specific data unless the cache key is user-bound. Below are concrete patterns and code examples.

1. Scope cache keys by user and tenant

Include authenticated user ID and tenant identifier in the cache key. In Buffalo, you can build a deterministic cache key from the session or from claims in the JWT.

import (
  "github.com/gobuffalo/buffalo"
  "github.com/gobuffalo/packr/v2"
  "github.com/golangid/cache"
)

// Example helper to build a user-scoped cache key
func userCacheKey(c buffalo.Context, collection string) string {
  userID := c.Session().GetString("user_id")
  tenant := c.Params().Get("tenant")
  if userID == "" {
    // Fallback to session ID if not authenticated for public endpoints
    userID = c.Session().ID()
  }
  return "user:" + userID + ":tenant:" + tenant + ":collection:" + collection
}

// Usage in a handler
func ShowProject(c buffalo.Context) error {
  projectID := c.Params().Get("project_id")
  key := userCacheKey(c, "projects") + ":project:" + projectID
  var project map[string]interface{}
  found, err := cache.Get(key, &project)
  if err != nil || !found {
    // Fetch from Firestore with user-aware query
    project, err = fetchProjectFromFirestore(c, projectID)
    if err != nil {
      return c.Render(500, r.JSON(err))
    }
    cache.Set(key, project, cache.DefaultExpiration)
  }
  return c.Render(200, r.JSON(project))
}

2. Authorize each Firestore read and avoid caching sensitive documents

Always validate access against Firestore document paths using security rules or server-side checks, and do not cache responses that include sensitive fields unless the cache key is strictly user-bound.

import (
  "cloud.google.com/go/firestore"
  "context"
)

func fetchProjectFromFirestore(c buffalo.Context, projectID string) (map[string]interface{}, error) {
  client, err := firestore.NewClient(c.Request().Context(), "your-project-id")
  if err != nil {
    return nil, err
  }
  defer client.Close()

  docRef := client.Collection("projects").Doc(projectID)
  // Optional: perform an additional authorization check using the request context
  // e.g., ensure the user has access to this project via a memberships subcollection
  doc, err := docRef.Get(c.Request().Context())
  if err != nil {
    return nil, err
  }
  if !doc.Exists() {
    return nil, nil
  }
  return doc.Data(), nil
}

3. Normalize inputs to prevent injection into Firestore queries

Validate and sanitize parameters before using them in Firestore queries to prevent injection of unexpected fields that could change the result set and poison the cache.

import (
  "github.com/gobuffalo/validate/v3"
)

func buildProjectQuery(c buffalo.Context, coll *firestore.CollectionRef) (*firestore.Query, error) {
  q := coll
  userID := c.Session().GetString("user_id")
  // Normalize and validate input
  if userID == "" {
    return nil, errors.New("missing user_id")
  }
  // Ensure no attacker-supplied field names are used
  q = q.Where("owner_id", "==", userID)
  return q, nil
}

4. Use short TTLs and avoid caching personalized responses when unsure

For endpoints that return user-specific data, prefer short-lived caches or skip caching entirely. When caching is necessary, tie the cache key tightly to the user and tenant and set a conservative expiration to reduce the window for poisoned cache reuse.

MiddleBrick scans can surface misconfigured cache scoping and data exposure findings by correlating Firestore query patterns with response caching behavior. The Pro plan’s continuous monitoring can alert you when new endpoints introduce cache-sensitive behavior, helping you maintain secure cache key design as your API evolves.