Injection Flaws in Sails with Mongodb

Injection flaws occur when untrusted data is interpreted as part of a command or query. In a Sails application using MongoDB, the risk centers on how criteria objects are built from user input before they are passed to MongoDB methods such as Model.find() or Model.aggregate(). If input is not validated and sanitized, an attacker can inject operators or nested structures that change the query semantics.

Sails is built on Waterline, an ORM-like layer that abstracts database operations. With the MongoDB adapter, your models typically use MongoDB-native syntax within Waterline’s criteria. While Waterline provides some protections, it does not automatically sanitize all inputs, especially when you pass raw objects or use where with JSON-like structures. For example, a developer might write:

// Potentially unsafe criteria built from request parameters
const criteria = {
  status: req.query.status,
  createdAt: {
    $gte: req.query.startDate ? new Date(req.query.startDate) : undefined
  }
};
const results = await User.find(criteria);

If req.query.status contains a MongoDB operator such as { $ne: null } or { $in: ['admin', 'root'] }, Waterline may pass it through, leading to unintended data access or data leakage. This is a classic injection vector where user-controlled data modifies query logic.

Additionally, aggregation pipelines are powerful but risky. An attacker may attempt to inject stages via parameters that are concatenated into the pipeline. For instance:

// Unsafe aggregation with user input
const matchStage = {
  $match: {
    role: req.query.role || {}
  }
};
const logs = await Log.aggregate([matchStage]);

If req.query.role includes operators like { $where: "return true" } or nested pipeline stages, the aggregation behavior changes in unexpected ways. MongoDB’s operators, such as $gt, $regex, and $where, can be abused to bypass intended filters, enumerate data, or perform denial-of-service via complex or malicious expressions.

LLM/AI Security checks are relevant here because system prompt leakage patterns sometimes resemble injection payloads, and output scanning ensures that sensitive data returned from queries is not inadvertently exposed. Injection flaws in Sails with MongoDB are particularly dangerous because they can lead to mass data exposure, privilege escalation, or unauthorized administrative actions if the compromised query runs with elevated context.

Remediation focuses on strict input validation, operator allowlisting, and avoiding direct passthrough of user input into MongoDB criteria or aggregation pipelines. Use schema validation libraries or explicit checks to ensure values conform to expected formats before they reach your models.

First, validate and sanitize all incoming data. For enumerated fields like status or role, use an allowlist:

const allowedStatuses = ['active', 'inactive', 'pending'];
const status = allowedStatuses.includes(req.query.status) ? req.query.status : 'active';

Second, avoid passing raw user input directly into MongoDB operators. Instead, construct safe values explicitly:

// Safe criteria construction
const criteria = {
  status: status,
  createdAt: {
    $gte: req.query.startDate ? new Date(req.query.startDate) : undefined
  }
};
const results = await User.find(criteria);

Third, when using aggregations, validate each stage and avoid dynamic injection of operators. Prefer hardcoded stages or transform user input into safe parameters:

// Safe aggregation with controlled input
const role = ['admin', 'user'].includes(req.query.role) ? req.query.role : 'user';
const matchStage = {
  $match: {
    role: role
  }
};
const logs = await Log.aggregate([matchStage]);

Fourth, use MongoDB’s schema validation features on the server side to enforce document structure and reject unexpected operators. While this does not replace application-level validation, it provides a safety net.

Finally, review dependencies and ensure your Waterline MongoDB adapter is up to date to benefit from any upstream security improvements. Combine these practices with middleBrick scans to detect injection risks during development and in CI/CD pipelines using the GitHub Action or CLI tool.