Buffer Overflow in Feathersjs with Cockroachdb

A buffer overflow in a Feathersjs service that uses Cockroachdb typically arises when unbounded input is accepted by a Feathers hook or service method and then forwarded to the database layer without length validation. Cockroachdb, like other SQL databases, accepts query parameters, but if a Featherjs service builds queries by concatenating user-controlled strings into SQL fragments (for example, using raw query builders or dynamic WHERE clauses), an attacker can supply oversized payloads that exceed expected buffers in the application layer or in client drivers before reaching the database.

Feathersjs applications often expose CRUD endpoints that accept arbitrary JSON payloads for create and update operations. If input validation is missing, an attacker can submit fields with extremely long strings or deeply nested structures. When these values are used to construct dynamic SQL, interpolate into identifiers, or populate large text columns without truncation or encoding, the request may consume excessive memory in the Feathersjs process or in protocol parsers between the client and Cockroachdb. Although Cockroachdb itself enforces size limits on rows and columns, the overflow risk is elevated when Feathersjs pre-processes data into formats that exceed those limits only after validation has failed.

Another vector involves Feathersjs hooks that transform data before persistence. A hook that concatenates user input into SQL strings or builds dynamic query options can introduce oversized values into prepared statements if placeholders are improperly used. For example, using string interpolation instead of parameterized queries may bypass driver-level safeguards and allow crafted payloads to overflow intermediate buffers in the application runtime or in network serialization layers. This can lead to crashes or information disclosure rather than direct injection, but it reveals insecure handling of untrusted data in the Feathersjs layer.

Additionally, Cockroachdb drivers and ORMs used with Feathersjs may rely on client-side buffers for packet assembly. If a Feathersjs service streams large result sets or constructs large JSON responses without pagination, the cumulative data transferred can saturate buffers in HTTP or gRPC layers, causing instability. While Cockroachdb handles query execution with its own memory management, the exposure surface is widened when Feathersjs does not enforce size constraints on request bodies, query parameters, or computed fields that feed into database operations.

Using middleBrick to scan a Feathersjs API that interfaces with Cockroachdb can detect missing input validation, missing length checks on string fields, and unsafe query construction patterns that precede buffer overflow conditions. The scanner evaluates the unauthenticated attack surface and correlates findings with the OpenAPI spec to highlight endpoints where user-controlled data reaches database interaction points without adequate sanitization or size limiting.

Remediation focuses on strict input validation, parameterized queries, and avoiding dynamic SQL construction. Ensure Feathersjs services validate payload sizes and sanitize string fields before they reach database interaction code.

1. Use Feathers hooks for size validation

Define a custom hook that checks string lengths and rejects oversized payloads before they reach the service method.

// src/hooks/validate-length.js
module.exports = function validateLength(options = {}) {
  return async context => {
    const maxLength = options.maxLength || 1000;
    const data = context.data;
    const traverse = (obj) => {
      if (typeof obj === 'string' && obj.length > maxLength) {
        throw new Error(`String exceeds maximum length of ${maxLength}`);
      }
      if (Array.isArray(obj)) {
        obj.forEach(traverse);
      } else if (obj !== null && typeof obj === 'object') {
        Object.values(obj).forEach(traverse);
      }
    };
    traverse(data);
    return context;
  };
};

// In your service file
const { Service } = require('feathersjs');
const validateLength = require('./hooks/validate-length');

class MyService extends Service {
  // service logic
}

const myService = new MyService();
myService.hooks({
  before: {
    create: [validateLength({ maxLength: 500 })],
    update: [validateLength({ maxLength: 500 })],
    patch: [validateLength({ maxLength: 500 })]
  }
});

2. Use parameterized queries with node-postgres or similar drivers

When using a Cockroachdb driver directly, always use placeholders and pass values separately to avoid concatenation risks. Below is an example using pg-compatible drivers that work with Cockroachdb.

// src/services/data/data.class.js
const { Service } = require('feathersjs');
const { Client } = require('pg'); // Cockroachdb compatible driver

class CockroachService extends Service {
  async create(data, params) {
    const client = new Client({
      connectionString: process.env.COCKROACHDB_URI
    });
    await client.connect();
    try {
      // Safe parameterized query
      const query = 'INSERT INTO users(name, email, bio) VALUES($1, $2, $3) RETURNING id';
      const values = [data.name, data.email, data.bio];
      const result = await client.query(query, values);
      return result.rows[0];
    } finally {
      await client.end();
    }
  }

  async update(id, data, params) {
    const client = new Client({
      connectionString: process.env.COCKROACHDB_URI
    });
    await client.connect();
    try {
      // Safe parameterized update
      const query = 'UPDATE users SET name=$1, email=$2, bio=$3 WHERE id=$4 RETURNING id';
      const values = [data.name, data.email, data.bio, id];
      const result = await client.query(query, values);
      return result.rows[0];
    } finally {
      await client.end();
    }
  }
}

module.exports = function init() {
  const app = this;
  app.use('/cockroach-data', new CockroachService());
};

3. Avoid dynamic SQL and identifier interpolation

Never interpolate user input into SQL strings. If dynamic queries are required, use a query builder that enforces parameterization and validates identifiers through a whitelist.

// Unsafe pattern to avoid
// const query = `SELECT * FROM users WHERE name = '${userInput.name}'`;

// Safer alternative using a parameterized approach even for dynamic fields
async function safeFindByFields(fields, values) {
  const client = new Client({ connectionString: process.env.COCKROACHDB_URI });
  await client.connect();
  try {
    const columns = Object.keys(fields);
    // Validate columns against a whitelist to prevent identifier injection
    const allowedColumns = ['name', 'email', 'status', 'created_at'];
    for (const col of columns) {
      if (!allowedColumns.includes(col)) {
        throw new Error('Invalid column name');
      }
    }
    const setClause = columns.map((col, i) => `${col}=$${i + 1}`).join(', ');
    const query = `UPDATE users SET ${setClause} WHERE id=$` + (columns.length + 1);
    const values = [...Object.values(fields), id];
    const result = await client.query(query, values);
    return result.rows;
  } finally {
    await client.end();
  }
}

4. Enable middleBrick scans to surface risky patterns

Run middleBrick against your Feathersjs API endpoints that interact with Cockroachdb to identify endpoints where user input flows into database operations without sufficient validation or encoding. The tool can highlight inconsistencies between your OpenAPI spec and runtime behavior, helping you prioritize fixes for oversized payload handling and query construction issues.