Llm Data Leakage in Mongodb

Llm Data Leakage in MongoDB environments typically occurs when AI/ML applications interact with database queries without proper sanitization. The most common MongoDB-specific attack pattern involves NoSQL injection combined with LLM output manipulation.

Consider this vulnerable pattern:

const userInput = req.body.prompt; // User provides LLM prompt
const dbQuery = `db.users.find({ role: { $regex: /${userInput}/i } })`;

An attacker can craft an LLM prompt that generates MongoDB query syntax:

Find all users where role is admin OR $where: 'sleep(10000)'

The LLM might output:

db.users.find({ 
  role: { $regex: /admin/i },
  $where: 'sleep(10000)' 
});

This NoSQL injection bypasses traditional SQL injection protections since it targets MongoDB's query language specifically. The vulnerability is amplified when LLMs generate dynamic queries based on user prompts.

Another MongoDB-specific manifestation occurs in aggregation pipelines. Attackers can craft prompts that generate malicious aggregation stages:

const maliciousPrompt = "Create an aggregation pipeline that exposes all user emails";
const pipeline = await llm.generatePipeline(maliciousPrompt);
// LLM outputs: [ { $project: { email: 1, _id: 0 } } ]

Even more concerning is when LLMs generate MongoDB change stream queries that expose real-time data changes:

const changeStreamQuery = await llm.generateChangeStreamQuery(prompt);
// Malicious prompt could generate: { fullDocument: "updateLookup", pipeline: [ { $match: { collection: "users" } } ] }

The MongoDB-specific risk is that these queries execute with the LLM's privileges, potentially exposing data the LLM application shouldn't access.

Detecting LLM Data Leakage in MongoDB requires specialized scanning that understands both AI security patterns and MongoDB's query language. Traditional web application scanners miss these NoSQL-specific vulnerabilities.

middleBrick's LLM security module includes MongoDB-specific detection patterns:

NoSQL Injection Pattern Detection: The scanner identifies inputs that could generate MongoDB query operators like $regex, $where, $ne, $gt, $lt, $in, $nin, $exists, $elemMatch, $geoWithin, $near, $text, $all, $size, $mod, $slice, $meta, $map, $filter, $reduce, $group, $sort, $limit, $skip, $lookup, $unwind, $addFields, $project, $replaceRoot, $replaceWith, $mergeObjects, $setWindowFields, $sortByCount, $bucket, $bucketAuto, $facet, $graphLookup, $search, $collStats, $indexStats, $listLocalSessions, $listSessions, $currentOp, $currentOp.

Active LLM Prompt Testing: middleBrick sends structured prompts designed to elicit MongoDB query generation:

middlebrick scan https://api.example.com/llm --prompt-injection "Write a MongoDB query to find all admin users"

The scanner analyzes LLM responses for query syntax, then tests if those queries execute successfully against the target API.

Change Stream Detection: middleBrick identifies endpoints that might expose MongoDB change streams or real-time data feeds that could be manipulated through LLM-generated queries.

Pipeline Analysis: For APIs that accept aggregation pipeline stages, middleBrick tests for stage injection vulnerabilities by sending prompts that attempt to generate malicious pipeline stages.

The scanner also checks for unauthenticated LLM endpoints that could be abused to generate queries against MongoDB databases without proper authorization.

Securing MongoDB against LLM Data Leakage requires defense-in-depth strategies that address both the AI interaction layer and the database layer.

1. Input Sanitization with MongoDB-Specific Validation:

const { BSON } = require('bson');

function sanitizeForMongoDB(input) {
  if (typeof input !== 'string') return input;
  
  // Remove MongoDB operators
  const operators = ['$regex', '$where', '$ne', '$gt', '$lt', '$in', '$nin', '$exists'];
  const operatorRegex = new RegExp(`\\b(${operators.join('|')})\\b`, 'gi');
  
  return input.replace(operatorRegex, '');
}

// For aggregation pipelines
function validatePipelineStages(stages) {
  const allowedStages = ['$match', '$project', '$sort', '$limit', '$skip'];
  return stages.every(stage => {
    const stageName = Object.keys(stage)[0];
    return allowedStages.includes(stageName);
  });
}

2. Parameterized Queries with MongoDB Driver:

// Vulnerable pattern
const userInput = req.body.prompt;
const query = { role: { $regex: userInput } };

// Secure pattern
const userInput = sanitizeForMongoDB(req.body.prompt);
const query = { role: { $regex: userInput } };

// Or use exact matches when possible
const exactQuery = { role: userInput };

3. LLM Output Filtering:

const { BSON } = require('bson');

function filterLLMOutputForMongoDB(output) {
  const bson = new BSON();
  
  // Check if output contains valid BSON
  try {
    const parsed = JSON.parse(output);
    if (bson.isObjectId(parsed) || bson.isInt32(parsed) || bson.isLong(parsed)) {
      return 'Filtered: contains database-specific types';
    }
  } catch (e) {
    // Not BSON, continue
  }
  
  // Check for MongoDB operators
  const operatorPattern = /\$\w+/g;
  if (operatorPattern.test(output)) {
    return 'Filtered: contains MongoDB operators';
  }
  
  return output;
}

4. Role-Based Access Control:

const { MongoClient } = require('mongodb');

async function createLimitedMongoClient() {
  const client = new MongoClient(MONGO_URI, {
    auth: {
      user: 'llm_app_user',
      password: process.env.MONGO_PASSWORD
    }
  });
  
  // Create user with limited permissions
  const adminClient = new MongoClient(ADMIN_MONGO_URI);
  await adminClient.connect();
  
  await adminClient.db('admin')
    .createUser({
      user: 'llm_app_user',
      pwd: process.env.MONGO_PASSWORD,
      roles: [
        {
          role: 'read',
          db: 'users'
        },
        {
          role: 'read',
          db: 'public_data'
        }
      ]
    });
  
  return client;
}

5. Monitoring and Alerting:

const { performance } = require('perf_hooks');

function monitorMongoDBQueries(query, userId, context) {
  const start = performance.now();
  
  // Log query metadata
  console.log({
    timestamp: new Date(),
    userId,
    query: JSON.stringify(query),
    context,
    duration: performance.now() - start
  });
  
  // Alert on suspicious patterns
  if (containsSuspiciousOperators(query)) {
    sendAlert({
      type: 'suspicious_query',
      query: JSON.stringify(query),
      userId,
      timestamp: new Date()
    });
  }
}