Api Rate Abuse in Restify with Firestore

Rate abuse in a Restify service that uses Cloud Firestore typically occurs when an API endpoint performs frequent or unbounded reads/writes without adequate request governance. Firestore operations such as get(), add(), and update() are billable and can become targets for exhaustion or cost amplification when a public endpoint lacks effective rate limiting.

Consider a typical REST handler in Restify that queries a collection to fetch or write user data:

const restify = require('restify');
const { initializeApp } = require('firebase-admin');
const firestore = initializeApp().firestore();

const server = restify.createServer();
server.get('/users/:uid', async (req, res, next) => {
  try {
    const doc = await firestore.collection('users').doc(req.params.uid).get();
    if (!doc.exists) {
      return res.send(404, { error: 'not_found' });
    }
    res.send(200, doc.data());
    return next();
  } catch (err) {
    return next(new restify.InternalError('Internal error'));
  }
});

server.post('/users/:uid/actions', async (req, res, next) => {
  const batch = firestore.batch();
  const ref = firestore.collection('users').doc(req.params.uid);
  batch.update(ref, { lastAction: new Date(), actionCount: restify.plugins.bodyParser({ mapParams: false })(req, res, next)?.actionCount + 1 });
  await batch.commit();
  res.send(200, { status: 'ok' });
  return next();
});

server.listen(8080, () => console.log('Listening'));

In this setup, each HTTP request translates to at least one Firestore read or write. Without rate limiting, an attacker can rapidly invoke the endpoint to trigger high read volumes or costly batched writes, potentially leading to elevated costs or degraded performance. Firestore’s eventual consistency and autoscaling nature do not prevent rate-based abuse at the application layer; the service will still process and bill each operation.

The risk is compounded when endpoints accept parameters that directly map to document paths or queries without validation. For example, an endpoint like GET /users/:uid can be targeted with a high volume of distinct user IDs, forcing repeated document reads. Similarly, write-heavy endpoints that create or update documents on every request can be exploited to drive up write operations, which are more expensive than reads in Firestore billing.

These patterns map to common API security concerns such as BFLA (Business Function Level Authorization) and Rate Limiting failures. Even though the API is unauthenticated (public endpoints), the absence of throttling mechanisms allows abuse of critical operations. Attackers may not need authentication to flood the endpoint; they simply need a valid route and a tool like curl or a script to generate requests.

middleBrick detects such issues by scanning the unauthenticated attack surface of your Restify endpoints and correlating runtime behavior with the OpenAPI specification. It flags missing rate limiting and highlights endpoints where Firestore operations could be abused. Findings include severity, contextual guidance, and references to related frameworks like OWASP API Top 10 and PCI-DSS, helping teams prioritize fixes based on actual risk.

Mitigating rate abuse for Firestore-backed Restify services requires explicit request governance at the endpoint or global level. Implement token-bucket or fixed-window rate limiting using middleware before Firestore operations are invoked. Below are concrete Restify middleware examples and handler adjustments that reduce abuse potential while preserving functionality.

Global rate limiter with restify-plugins

Use restify-plugins to apply rate limiting across all routes. This ensures no Firestore read or write can bypass the throttle:

const restify = require('restify');
const restifyPlugins = require('restify-plugins');
const { initializeApp } = require('firebase-admin');
const firestore = initializeApp().firestore();

const server = restify.createServer();
server.use(restifyPlugins.acceptParser(server.acceptable));
server.use(restifyPlugins.queryParser());
server.use(restifyPlugins.bodyParser());
server.use(restifyPlugins.rateLimit({ 
  rate: 100,           // max 100 requests
  burst: 20,           // allow short bursts up to 20
  rateMeter: 'local'   // simple in-memory meter for example
}));

server.get('/users/:uid', async (req, res, next) => {
  const doc = await firestore.collection('users').doc(req.params.uid).get();
  if (!doc.exists) {
    return res.send(404, { error: 'not_found' });
  }
  res.send(200, doc.data());
  return next();
});

server.post('/users/:uid/actions', async (req, res, next) => {
  const batch = firestore.batch();
  const ref = firestore.collection('users').doc(req.params.uid);
  batch.update(ref, { lastAction: new Date(), actionCount: 1 });
  await batch.commit();
  res.send(200, { status: 'ok' });
  return next();
});

server.listen(8080, () => console.log('Listening'));

Per-endpoint throttling with custom logic

For finer control, use a lightweight in-memory map to track request counts per key (e.g., IP or user ID). This is useful when different endpoints have distinct risk profiles:

const rateLimitWindowMs = 60_000; // 1 minute
const maxRequestsPerWindow = 30;
const requestCounts = new Map();

function rateLimiter(req, res, next) {
  const key = req.headers['x-forwarded-for'] || req.connection.remoteAddress;
  const now = Date.now();
  const entry = requestCounts.get(key) || { count: 0, start: now };

  if (now - entry.start > rateLimitWindowMs) {
    entry.count = 1;
    entry.start = now;
  } else {
    entry.count += 1;
  }

  if (entry.count > maxRequestsPerWindow) {
    return next(new restify.ForbiddenError('Too many requests'));
  }

  requestCounts.set(key, entry);
  return next();
}

server.pre(rateLimiter);

// Firestore handlers remain unchanged but are now protected
server.get('/users/:uid', async (req, res, next) => {
  const doc = await firestore.collection('users').doc(req.params.uid).get();
  res.send(200, doc.exists ? doc.data() : { error: 'not_found' });
  return next();
});

Input validation and query scoping

Reduce Firestore surface area by validating and bounding queries. Reject requests that attempt to access disallowed fields or use excessively broad filters:

server.get('/reports/:userId', async (req, res, next) => {
  const { fields } = req.query;
  const allowedFields = new Set(['timestamp', 'level', 'message']);
  if (fields) {
    const selected = fields.split(',');
    const invalid = selected.filter(f => !allowedFields.has(f));
    if (invalid.length > 0) {
      return next(new restify.BadRequestError('Invalid fields'));
    }
  }
  const ref = firestore.collection('logs')
    .where('userId', '==', req.params.userId)
    .limit(100);
  const snapshot = await ref.get();
  const rows = snapshot.docs.map(d => d.data());
  res.send(200, rows);
  return next();
});

By combining global rate limits, per-endpoint throttling, and strict input validation, you significantly lower the risk of Firestore-related rate abuse. These changes align with checks performed by middleBrick’s Rate Limiting and Input Validation scans, which surface misconfigurations and suggest concrete remediation steps.

middleBrick’s dashboard and CLI can help you verify that these controls are reflected in your runtime behavior. Use the GitHub Action to enforce thresholds in CI/CD and the MCP Server to scan API designs directly from your IDE, ensuring that Firestore endpoints remain resilient against abuse.