Vulnerable Components in Fastapi with Firestore

When Fastapi services use Google Cloud Firestore as a backend, several recurring patterns can expose the attack surface. Firestore security rules are not a substitute for application-level authorization, and rule misconfigurations can allow broad read or write access. If rules are set to test mode or lack constraints on document and collection names, unauthenticated or over-privileged requests can enumerate data or modify records.

In Fastapi, route handlers often bind user input directly to Firestore document IDs or collection paths. Without strict validation, this enables BOLA/IDOR and BFLA-style issues: a user can iterate through predictable IDs to access or update other users’ data. For example, using an integer or UUID supplied by the client to fetch a document without verifying ownership can lead to horizontal privilege escalation.

Input validation gaps are especially critical with Firestore because queries can be influenced by client-controlled keys. If a handler passes unchecked parameters into queries, an attacker can manipulate query filters to return more data than intended or trigger errors that leak stack traces or Firestore metadata. Lack of rate limiting on endpoints that read or write Firestore can also enable enumeration or write amplification, increasing the impact of weak rules.

Firestore indexes and composite indexes can inadvertently expose data through query side channels. If an endpoint performs a query on a field that should be private but is backed by an index, an authenticated context with broader permissions might allow an attacker to infer the existence of records. Overly permissive rules combined with missing ownership checks in the application logic amplify this risk.

The LLM/AI Security checks available in middleBrick are relevant here because endpoints that expose Firestore-backed functionality may also expose prompts or data used by integrated AI features. System prompt leakage patterns, active prompt injection probes, and output scanning for API keys or PII can identify risks where AI components interact with Firestore-stored configuration or user content. This is distinct from generic API checks and highlights the importance of securing both data and AI workflows.

Remediation centers on strict ownership checks, parameterized queries, and hardened Firestore rules. Always resolve the authenticated user identity in Fastapi and use it to scope every Firestore operation. Avoid using raw user input as document IDs without normalization and validation.

Example: Safe document retrieval with user ownership

from fastapi import Depends, HTTPException, status
from google.cloud import firestore
from pydantic import BaseModel
import uuid

db = firestore.Client()

class Item(BaseModel):
    name: str
    value: int

def get_current_user_id() -> str:
    # Replace with your actual auth/session logic
    return "user-123"

async def get_user_item(item_id: str):
    user_id = get_current_user_id()
    # Use a composite path to enforce ownership at the database level
    doc_ref = db.collection("users").document(user_id).collection("items").document(item_id)
    doc = await doc_ref.get()
    if not doc.exists:
        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Item not found")
    return {"id": doc.id, **doc.to_dict()}

Example: Parameterized query with strict field validation

from fastapi import Depends, HTTPException, status
from google.cloud import firestore
from pydantic import BaseModel
from typing import List

db = firestore.Client()

class PublicItem(BaseModel):
    name: str
    sku: str

def get_public_items(allowed_category: str, limit: int = 10) -> List[PublicItem]:
    # Validate and bound the query to a safe field and collection
    if not allowed_category:
        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Category is required")
    if limit < 1 or limit > 50:
        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid limit")
    
    snapshot = (
        db.collection("public_items")
        .where("category", "==", allowed_category)
        .limit(limit)
        .stream()
    )
    results = []
    for doc in snapshot:
        data = doc.to_dict()
        # Ensure only expected fields are returned
        results.append(PublicItem(name=data.get("name"), sku=data.get("sku")).dict())
    return results

Firestore security rules guidance (complement, not replacement, for app checks)

• Avoid test mode rules in production. Use request.auth != null to enforce authentication where required.
• Scope writes to user-specific paths, e.g., allow write: if request.auth != null && request.auth.uid == request.resource.data.user_id;
• Use resource.data and request.resource.data to validate fields and reject unexpected keys.
• Prefer allow read, write: if false as a safe default and enable only required operations explicitly.

Operational practices

• Normalize and validate all identifiers before using them in document paths.
• Apply consistent field naming and avoid storing sensitive data in fields that may be indexed unintentionally.
• Implement application-level rate limiting on endpoints that query Firestore to reduce enumeration risks.
• Log access patterns and monitor for repeated 404s on user-controlled document paths as an indicator of probing.

By combining path-based scoping in Fastapi, strict input validation, and disciplined Firestore rules, you reduce the likelihood of BOLA/IDOR, BFLA, and data exposure issues in this stack. middleBrick scans can surface insecure rules and missing ownership checks, helping you prioritize fixes.