Nosql Injection in Cassandra

Nosql injection in Cassandra exploits the way queries are constructed and executed against the database. Unlike traditional SQL injection that targets relational databases, Cassandra injection leverages the Cassandra Query Language (CQL) and the database's flexible schema design. The vulnerability arises when application code dynamically constructs CQL queries using untrusted user input without proper sanitization.

The most common attack vector occurs when developers use string concatenation to build CQL queries. Consider this vulnerable pattern:

const userId = req.query.userId; // User-controlled input
const query = `SELECT * FROM users WHERE user_id = '${userId}'`;
const result = await client.execute(query);

An attacker could supply a userId value like abc' OR '1'='1, causing the query to become:

SELECT * FROM users WHERE user_id = 'abc' OR '1'='1'

This would return all users in the table, bypassing authentication entirely. Cassandra's CQL syntax allows for complex boolean expressions, making this particularly dangerous.

Another Cassandra-specific injection pattern involves the IN clause. When developers dynamically construct lists of values:

const ids = req.query.ids.split(','); // ['1', '2', '3']
const query = `SELECT * FROM users WHERE user_id IN (${ids.map(id => `'${id}'`).join(',')})`;

An attacker could supply 1,2,3) OR 1=1--, breaking out of the IN clause and injecting arbitrary conditions.

Cassandra's collection types (sets, lists, maps) introduce additional injection vectors. When querying nested collections:

const query = `SELECT * FROM users WHERE user_id = '${userId}' AND preferences['theme'] = '${theme}'`;

Attackers can manipulate the map key syntax to inject additional conditions or cause query errors that reveal database structure.

The Cassandra driver's ALLOW FILTERING clause, while useful for development, creates severe injection risks when combined with dynamic queries. An attacker could force expensive full-table scans:

SELECT * FROM users WHERE user_id = 'abc' ALLOW FILTERING OR 1=1

This not only bypasses intended filters but also creates denial-of-service conditions through resource exhaustion.

Detecting NoSQL injection in Cassandra requires both static analysis of application code and dynamic runtime scanning. The most effective approach combines multiple detection methods to identify vulnerable query construction patterns.

Static code analysis should flag these specific Cassandra anti-patterns:

// Vulnerable: String concatenation
const query = `SELECT * FROM users WHERE user_id = '${userId}'`;

// Vulnerable: Dynamic IN clauses
const query = `SELECT * FROM users WHERE id IN (${ids.join(',')})`;

// Vulnerable: ALLOW FILTERING with dynamic input
const query = `SELECT * FROM users WHERE name = '${name}' ALLOW FILTERING`;

// Vulnerable: Map access with dynamic keys
const query = `SELECT * FROM users WHERE preferences['${key}'] = '${value}'`;

Dynamic detection through runtime scanning can identify injection attempts by monitoring query execution patterns. Tools like middleBrick's API security scanner can detect these vulnerabilities by submitting specially crafted payloads and analyzing responses.

For Cassandra-specific detection, middleBrick's scanner tests for:

middleBrick's scanner specifically tests Cassandra endpoints by sending payloads designed to trigger CQL syntax errors or unexpected query results. The scanner analyzes response patterns to determine if injection is possible, then provides specific remediation guidance.

Additional detection methods include:

• Query logging analysis to identify unusual query patterns
• Database audit logging to track unexpected data access
• Application performance monitoring to detect sudden query execution time increases

For production environments, implementing query pattern analysis can help detect injection attempts in real-time by flagging queries that deviate from normal application behavior.

Remediating NoSQL injection in Cassandra requires adopting parameterized queries and query builder patterns that prevent untrusted input from affecting query structure. The Cassandra driver provides prepared statements that separate query structure from data.

The primary remediation approach uses prepared statements:

// Vulnerable pattern
const query = `SELECT * FROM users WHERE user_id = '${userId}'`;
const result = await client.execute(query);

// Secure pattern with prepared statements
const query = 'SELECT * FROM users WHERE user_id = ?';
const result = await client.execute(query, [userId], { prepare: true });

Prepared statements ensure user input is treated as data, not executable CQL code. The driver handles proper escaping and type conversion automatically.

For queries with multiple parameters, use array binding:

// Vulnerable: Dynamic IN clause
const ids = req.query.ids.split(',');
const query = `SELECT * FROM users WHERE id IN (${ids.map(id => `'${id}'`).join(',')})`;

// Secure: Prepared statements with array binding
const query = 'SELECT * FROM users WHERE id IN ?';
const result = await client.execute(query, [ids], { prepare: true });

Note that Cassandra requires the array to be passed as a single bound parameter, not individual values.

When working with collection types, use parameterized map access:

// Vulnerable
const query = `SELECT * FROM users WHERE preferences['${key}'] = '${value}'`;

// Secure
const query = 'SELECT * FROM users WHERE preferences[?]=?';
const result = await client.execute(query, [key, value], { prepare: true });

For complex queries involving conditional logic, use query builders that enforce parameterization:

const cassandra = require('cassandra-driver');
const queryBuilder = new cassandra.QueryBuilder('users');

queryBuilder.select();
queryBuilder.where('user_id').isEqualTo(userId);
queryBuilder.where('preferences[theme]').isEqualTo(theme);

const query = queryBuilder.toString();
const result = await client.execute(query, [userId, theme], { prepare: true });

Avoid using ALLOW FILTERING entirely in production applications. If filtering is necessary, design your data model with appropriate clustering keys and partition keys instead.

For legacy applications where refactoring to prepared statements isn't immediately possible, implement strict input validation:

function validateUserId(userId) {
  // Only allow alphanumeric IDs of specific length
  const userIdRegex = /^[a-zA-Z0-9_-]{8,32}$/;
  if (!userIdRegex.test(userId)) {
    throw new Error('Invalid user ID format');
  }
  return userId;
}

const userId = validateUserId(req.query.userId);
const query = `SELECT * FROM users WHERE user_id = '${userId}'`;
const result = await client.execute(query);

This validation approach is less secure than prepared statements but provides a temporary mitigation layer while refactoring.