Insecure Direct Object Reference in Cassandra

Apache Cassandra's distributed architecture creates unique Insecure Direct Object Reference (IDOR) vulnerabilities that differ from traditional relational databases. The combination of partition keys, clustering columns, and Cassandra's eventual consistency model introduces attack vectors that developers often overlook.

In Cassandra, IDOR typically manifests when applications construct CQL queries using user-supplied partition keys or clustering columns without proper validation. Consider this common pattern:

const query = ‘SELECT * FROM users WHERE user_id = ‘ + userId + ‘;’;
const results = await session.execute(query);

The vulnerability here isn't just SQL injection—it's the fundamental assumption that users should be able to specify arbitrary partition keys. In Cassandra, partition keys determine which nodes store data, so an attacker who guesses valid partition keys can access any user's data by simply changing the key value.

Clustering column manipulation presents another Cassandra-specific IDOR vector. When applications allow users to specify clustering column values, they expose the logical ordering of data:

const query = ‘SELECT * FROM orders WHERE user_id = ? AND order_date > ?;’;
const results = await session.execute(query, [userId, date]);

If the application doesn't verify that the authenticated user actually owns the user_id being queried, an attacker can enumerate through user IDs to access other users' orders.

Cassandra's collection types (lists, sets, maps) introduce additional complexity. Applications often expose collection elements through APIs that construct CQL like:

const query = ‘SELECT tags[user_id] FROM products WHERE product_id = ?;’;
const results = await session.execute(query, [productId]);

This pattern assumes the user_id in the collection corresponds to the authenticated user, but without server-side validation, attackers can manipulate the user_id parameter to access arbitrary collection elements.

Materialized views in Cassandra create another IDOR surface. Since materialized views replicate data across different partition keys, an application might inadvertently expose data through a view that wasn't intended to be accessible:

const query = ‘SELECT * FROM user_activity_by_date WHERE date = ?;’;
const results = await session.execute(query, [date]);

If the application doesn't filter by user_id after querying the materialized view, any authenticated user can see all activity for any date.

Detecting IDOR vulnerabilities in Cassandra requires understanding both the data model and query patterns. Static analysis tools often miss these issues because they don't understand Cassandra's partition key semantics.

Code review should focus on identifying where partition keys and clustering columns are constructed from user input. Look for patterns where CQL queries are built dynamically:

function getUserData(userId) {
  const query = ‘SELECT * FROM users WHERE user_id = ‘ + userId;
  return session.execute(query);
}

The red flag here is that user_id appears directly in the query without any validation that the requesting user has permission to access that specific user_id.

Dynamic table name construction is another Cassandra-specific IDOR pattern:

const tableName = ‘user_’ + userId + ‘_data’;
const query = ‘SELECT * FROM ‘ + tableName + ‘ WHERE timestamp > ?;’;;
const results = await session.execute(query, [timestamp]);

This allows attackers to enumerate through table names by changing the userId parameter, potentially accessing data from tables they shouldn't see.

middleBrick's Cassandra-specific scanning detects these patterns through black-box testing. The scanner attempts to access data using systematically varied partition keys and clustering columns to identify whether the application properly validates authorization. For example, it tests:

• Sequential partition key enumeration (user_1, user_2, user_3...)
• Boundary value testing for clustering columns
• Collection element access with manipulated keys
• Materialized view access without proper user filtering

The scanner also analyzes the application's API surface to identify endpoints that might construct CQL queries from URL parameters, headers, or request bodies. It then attempts authenticated access with different user contexts to verify that data isolation is properly enforced.

For applications using Cassandra with microservices, middleBrick can detect IDOR vulnerabilities that arise from improper service-to-service authentication. If one service queries another service's Cassandra data without validating the end user's permissions, this creates a vulnerability that middleBrick's inter-service scanning can identify.

Remediating IDOR vulnerabilities in Cassandra requires a defense-in-depth approach that combines proper query construction, authorization checks, and data model design.

The first principle is never to trust partition keys or clustering columns from untrusted sources. Always validate that the authenticated user has permission to access the requested partition:

async function getUserData(authUser, requestedUserId) {
  // Verify ownership before querying
  if (authUser.id !== requestedUserId) {
    throw new Error(‘Access denied’);
  }

  const query = ‘SELECT * FROM users WHERE user_id = ?;’;
  const results = await session.execute(query, [requestedUserId]);
  return results.first();
}

For scenarios where users legitimately need to access data across partitions (like an admin viewing any user's data), implement role-based access control at the application layer:

async function getUserData(authUser, requestedUserId) {
  if (!hasPermission(authUser, ‘view_user_data’)) {
    throw new Error(‘Insufficient permissions’);
  }

  const query = ‘SELECT * FROM users WHERE user_id = ?;’;
  const results = await session.execute(query, [requestedUserId]);
  return results.first();
}

Cassandra's lightweight transactions (LWT) can help prevent IDOR in concurrent scenarios. When updating data, use conditional writes to ensure the user owns the data being modified:

async function updateUserData(authUser, userData) {
  const query = ‘UPDATE users SET email = ?, name = ? WHERE user_id = ? IF EXISTS;’;
  const results = await session.execute(query, [
    userData.email,
    userData.name,
    authUser.id
  ]);

  if (!results.first().getBoolean(0)) {
    throw new Error(‘Update failed or unauthorized’);
  }
}

For collection-based IDOR vulnerabilities, validate collection membership before access:

async function getUserTag(authUser, userId, tagId) {
  // Verify user owns the tag
  const query = ‘SELECT tags[?] FROM users WHERE user_id = ?;’;
  const results = await session.execute(query, [tagId, userId]);
  
  if (results.first() === null) {
    throw new Error(‘Tag not found or unauthorized’);
  }
  
  return results.first().getFrozen(0);
}

When using materialized views, always re-apply user-based filtering in the application layer:

async function getUserActivity(authUser, date) {
  const query = ‘SELECT * FROM user_activity_by_date WHERE date = ?;’;
  const results = await session.execute(query, [date]);
  
  // Filter by user_id in application code
  return results.all().filter(activity => activity.user_id === authUser.id);
}

For Cassandra applications with complex authorization requirements, consider implementing row-level security using separate permission tables:

async function canAccessResource(authUser, resourceId, resourceType) {
  const query = ‘SELECT COUNT(*) FROM permissions 
                WHERE user_id = ? AND resource_id = ? AND resource_type = ?;’;
  const results = await session.execute(query, [authUser.id, resourceId, resourceType]);
  return results.first().getLong(0) > 0;
}