Prototype Pollution in Buffalo with Firestore

Prototype pollution in a Buffalo application that uses Firestore typically arises when untrusted input is merged into objects that later influence how Firestore document data is read, transformed, or passed to client-side code. Buffalo’s default setup encourages strong separation between database operations and in-memory object manipulation, but developers can inadvertently introduce risks by constructing Firestore document references or query parameters from user-controlled data without proper validation or sanitization.

Consider a scenario where an endpoint builds a Firestore document map using request query parameters or JSON payload fields. If an attacker supplies a payload such as { "__proto__": { "isAdmin": true } } or { "constructor": { "prototype": { "polluted": true } } }, and this input is shallow-merged into a JavaScript object that is later used to assemble a Firestore write or to derive a document path, the polluted prototype can affect behavior across the runtime. In Buffalo, this may manifest when helper functions prepare data for Create or Update operations against a Firestore collection, especially when using variadic argument patterns or reflection-based mapping that do not exclude prototype properties.

Because Firestore documents are often serialized for client consumption (for example, through Firestore listeners or REST exports), polluted prototypes can lead to unexpected deserialization behavior on the client. An attacker might cause additional fields to appear in deserialized objects, trigger falsy conditions in equality checks, or interfere with framework-specific object lifecycle methods. While Firestore itself does not execute JavaScript, the surrounding Buffalo application may use the retrieved data to make authorization decisions or construct further queries. For instance, using a polluted object to derive a document reference path or to conditionally include fields in a query could bypass intended visibility rules, effectively exposing data that should remain restricted.

Prototyping risks are compounded when combined with Firestore’s flexible data model. A developer might use user input to dynamically set field paths or map query results into structured objects. Without strict schema validation and input sanitization, an attacker can inject properties such as constructor or prototype into nested objects that later influence how Firestore document snapshots are interpreted. This is particularly relevant when Buffalo handlers marshal Firestore results into domain structs using generic assignment or reflection, as inherited properties can silently alter struct behavior.

To detect these issues, middleBrick performs 12 security checks in parallel, including Input Validation and Property Authorization, which help surface risky data flows between client-supplied data and Firestore operations. In scenarios where unauthenticated endpoints expose document structures or where LLM endpoints are reachable, the LLM/AI Security checks can identify prototype pollution vectors that may facilitate prompt injection or data exfiltration through crafted inputs. By correlating runtime findings with OpenAPI/Swagger specifications, including full $ref resolution, middleBrick helps teams trace how untrusted input could propagate into Firestore-related logic without executing or modifying backend code.

Remediation focuses on preventing untrusted input from influencing object prototypes and from being directly forwarded to Firestore operations. In Buffalo, this involves strict validation and canonicalization of incoming data, avoiding reflection-based merging, and explicitly whitelisting fields that are safe to use in Firestore document paths or queries.

One effective pattern is to unmarshal request payloads into concrete structs rather than using map[string]interface{} with generic merging. Define structs that mirror the expected Firestore document shape and use binding.Decode to validate and parse input. This approach naturally rejects unexpected keys such as __proto__ or constructor when using strict decoding options, reducing the risk of prototype pollution before data reaches Firestore logic.

When constructing Firestore documents, prefer explicit field assignment instead of dynamic property copying. For example, instead of iterating over raw input and assigning values to a map that is passed to client.Collection("items").Doc(id).Set, map known fields individually. Below is a concrete Buffalo handler that demonstrates safe Firestore document creation using Firestore Go SDK and validated input:

// models/item.go
type Item struct {
    Name  string  `json:"name" validate:"required,printascii"`
    Value string  `json:"value" validate:"required,max=200"`
    Owner string  `json:"owner" validate:"required"`
}

// handlers/item.go
func CreateItem(c buffalo.Context) error {
    item := &Item{}
    if err := c.Bind(item); err != nil {
        return c.Render(400, r.JSON(Error{Message: err.Error()}))
    }
    if err := c.Validate(item); err != nil {
        return c.Render(422, r.JSON(Error{Message: err.Error()}))
    }
    ctx := context.Background()
    client, err := firestore.NewClient(ctx, <your-project>)
    if err != nil {
        return c.Render(500, r.JSON(Error{Message: "internal error"}))
    }
    docRef := client.Collection("items").Doc(item.Owner + "_" + item.Name) // safe: derived from validated fields
    _, err = docRef.Set(ctx, map[string]interface{}{
        "value": item.Value,
        "owner": item.Owner,
        "name":  item.Name,
    })
    if err != nil {
        return c.Render(500, r.JSON(Error{Message: "firestore error"}))
    }
    return c.Render(201, r.JSON(item))
}

For queries, avoid using user input to construct field paths or map keys that could inherit prototype properties. Instead, use hardcoded field selectors and parameterized filters. If Firestore document IDs must be derived from user input, sanitize them to exclude dots, slashes, and reserved characters, and avoid concatenating raw input into document paths without canonicalization.

In a Buffalo SPA setup, ensure that any Firestore data exposed to the frontend is serialized through controlled view models rather than raw document snapshots. This prevents polluted properties from being deserialized on the client side. middleBrick’s Input Validation and Data Exposure checks can help identify endpoints where Firestore documents are returned without sufficient sanitization, supporting compliance with OWASP API Top 10 and relevant data protection frameworks.