Insecure Design in Fastapi with Firestore

Insecure design occurs when application architecture and data-flow decisions expose sensitive operations or over-permissive access patterns. When Fastapi services interact with Google Cloud Firestore, several design choices can weaken authorization and data integrity. Firestore relies on careful rules configuration and token-based identity handling; if Fastapi endpoints do not validate identities and enforce least privilege before issuing Firestore operations, the API surface becomes vulnerable to IDOR, privilege escalation, and data exposure.

Consider a design where Fastapi constructs Firestore client instances per request using a service account with broad project access, and then routes user-supplied identifiers directly into document paths without ownership checks. For example, an endpoint like /users/{user_id}/settings may retrieve a document using db.collection("users").document(user_id) without verifying that the authenticated requestor owns that user_id. This design bypasses any runtime authorization layer and relies only on Firestore security rules, which may be misconfigured or bypassed if tokens are spoofed or if the service account key is inadvertently exposed. Such a design also encourages concatenating paths from request data, enabling path traversal or unintended multi-tenancy leaks.

Another insecure pattern is assuming Firestore rules alone will protect sensitive fields. If Fastapi returns full documents to clients without filtering sensitive keys (e.g., internal flags, administrative roles), the API design effectively duplicates trust to the client. Combined with missing rate limiting at the Fastapi layer, this enables mass enumeration or brute-force enumeration of user IDs. The API may also accept query parameters that alter Firestore queries in unsafe ways, such as passing raw field names for ordering or filtering, which can expose unintended indices or allow injection-like behavior through crafted input.

LLM/AI Security considerations intersect here when endpoints expose model-related endpoints or logs that include prompts or system messages. If Fastapi routes for LLM features do not enforce strict authentication and tightly scoped tokens, they may become unauthenticated endpoints that disclose system prompts or allow prompt injection against integrated AI features. This expands the insecure design to include AI-specific vectors, where improperly isolated Firestore-stored prompts or agent configurations could be enumerated or manipulated.

Mapping to known standards, insecure design patterns align with OWASP API Top 10:2023 Broken Object Level Authorization (BOLA)/IDOR, Improper Asset Management, and Security Misconfiguration. PCI-DSS, SOC 2, HIPAA, and GDPR similarly expect controlled access to personal data and auditability of access patterns. middleBrick detects these design risks by correlating OpenAPI/Swagger specs (with full $ref resolution) against runtime behavior, highlighting endpoints that accept user-controlled identifiers without ownership validation or that expose excessive data scopes.

Remediation centers on enforcing ownership checks, scoping Firestore access, and validating inputs before queries. Use Firebase Admin SDK to obtain a Firestore client with elevated privileges only within secure server code, and never expose service account keys to clients. Implement per-request identity resolution in Fastapi, verify that the requesting user owns the target resource, and scope queries to their tenant or user partition.

Example secure Fastapi route with Firestore:

from fastapi import FastAPI, Depends, HTTPException, status
from google.cloud import firestore
from firebase_admin import auth, initialize_app, credentials
import os

app = FastAPI()

# Initialize once at startup
cred = credentials.Certificate(os.getenv("FIRESTORE_ADMIN_CRED"))
admin_app = initialize_app(cred)
db = firestore.client(app=admin_app)

def get_current_user(authorization: str = Depends(oauth2_scheme)):
    token = authorization.split(" ")[1] if authorization.startswith("Bearer ") else None
    if not token:
        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Missing token")
    try:
        decoded = auth.verify_id_token(token)
        return decoded
    except auth.InvalidIdTokenError:
        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token")

@app.get("/users/{user_id}/settings")
def get_user_settings(user_id: str, user: dict = Depends(get_current_user)):
    # Enforce BOLA: ensure the authenticated user owns this user_id
    if user["user_id"] != user_id:
        raise HTTPException(status_code=403, detail="Access denied")
    doc_ref = db.collection("users").document(user_id)
    doc = doc_ref.get()
    if not doc.exists:
        raise HTTPException(status_code=404, detail="Settings not found")
    # Filter sensitive internal fields before returning
    data = doc.to_dict()
    return {"preferences": data.get("preferences"), "theme": data.get("theme")}

Key design fixes illustrated:

• Ownership check: compare authenticated user identity (from verified ID token) with the requested resource identifier before any Firestore read.
• Scoped access: use Admin SDK server-side only; never create Firestore clients on the client side or with broad project permissions exposed.
• Output filtering: explicitly select safe fields to avoid leaking internal metadata or flags stored in the document.
• Path construction: avoid concatenating untrusted input into document paths; use parameterized collections and document IDs validated against a pattern or lookup.

For queries that list user-owned resources, scope to the authenticated user’s partition:

@app.get("/users/me/items")
def list_user_items(user: dict = Depends(get_current_user)):
    items = db.collection("items").where("owner_id", "==", user["user_id"]).stream()
    return [{"id": item.id, "name": item.to_dict().get("name")} for item in items]

Rate limiting and input validation should be enforced at the Fastapi layer (e.g., using middleware or libraries like slowapi) to complement Firestore rules. Always treat Firestore rules as a last line of defense, not the primary authorization mechanism. middleBrick’s scans can validate these designs by checking whether endpoints rely solely on rules and whether exposed queries allow unsafe filtering or path manipulation.