Insufficient Logging in Fiber with Firestore

Insufficient logging in a Fiber application that interacts with Firestore reduces visibility into authentication, authorization, and data access events. Without structured logs that capture who accessed what data and when, security teams cannot reliably detect suspicious patterns such as unusual read rates, repeated failed lookups, or anomalous query structures that may indicate enumeration or injection attempts.

When Firestore operations lack detailed request context—such as the authenticated principal (if any), document IDs accessed, query filters, and timestamps—correlating events across services becomes difficult. For example, an attacker probing user IDs via BOLA/IDOR may try hundreds of document references; without per-request logs that include the attempted ID and response status, these probes can go unnoticed. Similarly, missing logs for rule evaluation outcomes and permission denials weakens auditability required for compliance frameworks such as SOC 2 and GDPR.

In a black-box scan, middleBrick runs 12 security checks in parallel, including Authentication, BOLA/IDOR, and Data Exposure. For an API using Firestore, the scanner observes runtime behavior rather than source code, so it relies on observable outputs and response patterns. Insufficient logging on the server side can reduce the signal available to the scanner, but the scanner can still surface findings when endpoints expose information leakage through verbose errors or missing audit trails. For instance, if error messages reveal Firestore document paths or internal IDs, this becomes an actionable finding under Data Exposure and Input Validation checks.

Moreover, because Firestore security rules are evaluated at runtime, logging the outcome of those evaluations (allow/deny with reason) is essential for detecting logic flaws such as over-permissive reads or misconfigured ownership checks. Without such logs, it is difficult to verify whether rules align with intended access patterns, increasing the risk of privilege escalation or unauthorized data access. middleBrick’s LLM/AI Security checks highlight additional concerns when endpoints expose system prompts or generate outputs containing API keys or PII; insufficient logging can compound these risks by failing to record how model-integrated endpoints interact with Firestore.

To strengthen visibility, ensure each Fiber route that performs Firestore operations emits structured logs containing at minimum: request ID, timestamp, user context (or anon indicator), operation type (get, set, query), document path, query constraints, and authorization result. This aligns with findings from the Inventory Management and Property Authorization checks, which assess whether endpoints expose implementation details or insufficient access controls. middleBrick’s dashboard can then track these findings over time and, with the Pro plan, integrate continuous monitoring and alerts to detect abnormal log patterns indicative of attack or misconfiguration.

Remediation focuses on enriching request context and standardizing outcomes for Firestore interactions in Fiber. Use middleware to inject a request-scoped correlation ID, attach user identity when available, and ensure consistent error handling that does not leak Firestore paths or internal details.

Structured Logging Middleware

Add middleware in Fiber that creates a request ID and logs key Firestore metadata for every route that accesses Firestore:

const { app } = require('fastify')(); // Using Fiber-compatible pattern
const { initializeApp } = require('firebase-admin');
const { getFirestore } = require('firebase-admin/firestore');
const { v4: uuidv4 } = require('uuid');

initializeApp({
  credential: /* your credential */
});
const db = getFirestore();

app.use((req, res, next) => {
  req.id = uuidv4();
  const start = Date.now();
  res.locals = res.locals || {};
  res.locals.startTime = start;
  res.locals.requestId = req.id;
  next();
});

function firestoreLogger(req, res, next) {
  res.on('finish', () => {
    const duration = Date.now() - res.locals.startTime;
    const userId = req.user ? req.user.uid : 'anonymous';
    const path = req.originalUrl;
    const method = req.method;
    console.log(JSON.stringify({
      request_id: res.locals.requestId,
      timestamp: new Date().toISOString(),
      user_id: userId,
      method,
      path,
      status: res.statusCode,
      duration_ms: duration
    }));
  });
  next();
}
app.use(firestoreLogger);

Instrumented Firestore Operations

For each route performing Firestore actions, log inputs, query constraints, and outcomes. The following example shows a GET endpoint that retrieves a user document by ID with structured logging and safe error handling:

app.get('/users/:uid', async (req, res) => {
  const requestId = res.locals.requestId;
  const uid = req.params.uid;
  const userId = req.user ? req.user.uid : 'anonymous';
  try {
    const docRef = db.collection('users').doc(uid);
    const snapshot = await docRef.get();
    if (!snapshot.exists) {
      console.log(JSON.stringify({
        request_id: requestId,
        timestamp: new Date().toISOString(),
        user_id: userId,
        operation: 'get',
        path: `users/${uid}`,
        result: 'not_found'
      }));
      return res.status(404).send({ error: 'Not found' });
    }
    console.log(JSON.stringify({
      request_id: requestId,
      timestamp: new Date().toISOString(),
      user_id: userId,
      operation: 'get',
      path: `users/${uid}`,
      result: 'success',
      data_keys: Object.keys(snapshot.data() || {})
    }));
    res.send(snapshot.data());
  } catch (err) {
    console.log(JSON.stringify({
      request_id: requestId,
      timestamp: new Date().toISOString(),
      user_id: userId,
      operation: 'get',
      path: `users/${uid}`,
      result: 'error',
      error_code: err.code || 'unknown'
    }));
    res.status(500).send({ error: 'Internal error' });
  }
});

Query and Batch Operation Logging

When using queries or transactions, log the query constraints and document IDs examined to support investigation and to align with Property Authorization and Input Validation checks:

app.get('/users/role/:role', async (req, res) => {
  const requestId = res.locals.requestId;
  const role = req.params.role;
  const userId = req.user ? req.user.uid : 'anonymous';
  try {
    const querySnap = await db.collection('users').where('role', '==', role).get();
    const ids = querySnap.docs.map(d => d.id);
    console.log(JSON.stringify({
      request_id: requestId,
      timestamp: new Date().toISOString(),
      user_id: userId,
      operation: 'query',
      collection: 'users',
      filter: { field: 'role', op: '==', value: role },
      matched_ids: ids,
      count: ids.length
    }));
    res.send({ ids });
  } catch (err) {
    console.log(JSON.stringify({
      request_id: requestId,
      timestamp: new Date().toISOString(),
      user_id: userId,
      operation: 'query',
      error_code: err.code || 'unknown'
    }));
    res.status(500).send({ error: 'Internal error' });
  }
});

By combining structured logs with consistent error handling, you improve auditability and provide the signals needed for runtime security checks. These practices complement middleBrick scans, which can surface findings related to Authentication, Data Exposure, and LLM Security when endpoints reveal sensitive information in responses. With the Pro plan, you can enable continuous monitoring to detect anomalies in log patterns over time, and the GitHub Action can fail builds if risk scores degrade.