Excessive Data Exposure in Chi with Mongodb

Excessive Data Exposure occurs when an API returns more data than necessary for a given operation. In Chi, a common pattern is to fetch a Mongodb document and send it directly as JSON without filtering sensitive fields. Because Chi applications often embed Mongodb queries inside route handlers, developers might return entire documents—including internal IDs, timestamps, or metadata—without stripping fields like passwords, tokens, or role flags. This becomes a risk when endpoints are unintentionally exposed through the public attack surface scanned by middleBrick. The scanner performs unauthenticated tests against the endpoint and can observe that responses include full Mongodb documents, revealing data that should be restricted.

For example, a GET /users/:id endpoint might query Mongodb with db.collection("users").findOne({_id: id}) and return the result as-is. If the document contains fields such as resetToken, isAdmin, or even hashed credentials, the response exposes them to any client that can call the endpoint. MiddleBrick’s checks for Data Exposure highlight this by correlating OpenAPI paths that return large, unfiltered payloads with runtime observations showing verbose Mongodb-like structures. Attackers can chain this with other weaknesses, such as Insecure Direct Object References, to enumerate or infer additional data.

Another scenario involves projection misuse. Developers sometimes build dynamic query objects without validating which fields are requested, leading to responses that include internal fields like __v (versioning) or nested metadata. When combined with missing rate limiting or weak authentication, excessive data from Mongodb can facilitate further attacks. middleBrick’s parallel checks for Input Validation and Authentication help detect whether responses vary significantly based on user context, indicating potential overexposure.

Using middleBrick’s scans, teams can see how endpoints that interact with Mongodb may leak information through verbose payloads. The tool’s findings include severity ratings and remediation guidance, helping developers understand that responses must be shaped to the minimum required set. This is especially important in Chi where idiomatic data access patterns can inadvertently expose entire documents if projection and filtering are not enforced explicitly.

To fix Excessive Data Exposure in Chi with Mongodb, explicitly define which fields are safe to return and apply projection in queries. Avoid returning entire documents. Instead, construct a clean response object or use a projection document that includes only necessary fields. For read operations, prefer lean queries to avoid Mongoose-specific metadata when using an ODM, or explicitly exclude sensitive fields when querying raw collections.

Example: Safe projection with native Mongodb driver in Chi

import { MongoClient } from "mongodb";
import { Router } from "@hono/zod-openapi";

const client = new MongoClient(process.env.MONGODB_URI);
const router = Router();

router.get("/users/:id", async (c) => {
  const id = c.req.param("id");
  await client.connect();
  const db = client.db("myapp");
  const user = await db.collection("users").findOne(
    { _id: id },
    { projection: { email: 1, name: 1, role: 1, _id: 1 } } // only safe fields
  );
  await client.close();
  if (!user) {
    return c.json({ error: "Not found" }, 404);
  }
  return c.json(user);
});

Example: Excluding sensitive fields with Mongoose lean and select

import express from "express";
import User from "../models/user";

const router = express.Router();

router.get("/profile/:id", async (req, res) => {
  try {
    const user = await User.findById(req.params.id)
      .select("name email role createdAt") // explicit allowlist
      .lean() // returns plain JS object, no Mongoose metadata
      .exec();
    if (!user) {
      return res.status(404).json({ error: "Not found" });
    }
    res.json(user);
  } catch (err) {
    res.status(500).json({ error: "Server error" });
  }
});

General remediation practices

• Define an allowlist of fields for each endpoint and enforce it in the query projection.
• Never rely on client-side filtering; remove sensitive data server-side before serialization.
• Avoid dynamic inclusion of all request fields (e.g., { $set: req.body }) when updating documents without validation.
• Use middleware to scrub responses if your framework supports response transformation, ensuring no accidental leakage of Mongodb metadata.

middleBrick’s scans can validate that endpoints applying these patterns do not return full documents. By combining secure coding practices with continuous monitoring plans available in Pro and Enterprise tiers, teams can reduce the window during which excessive data is exposed.