Bola Idor in Cassandra

BOLO/IdOR (Broken Object Level Authorization/Insecure Direct Object Reference) in Cassandra environments typically occurs when applications fail to properly validate whether a user has permission to access specific data rows. In Cassandra, this vulnerability manifests through several unique attack patterns due to its distributed architecture and query patterns.

The most common Cassandra-specific BOLA scenario involves partition key manipulation. Consider a healthcare application storing patient records where the partition key includes a hospital ID and patient ID. An attacker might modify the patient ID parameter in requests to access other patients' records:

SELECT * FROM patient_records WHERE hospital_id = '123' AND patient_id = ?;

If the application doesn't verify that the requesting user belongs to hospital '123' or has rights to patient '456', an attacker can simply change the patient_id value to access any record. This becomes particularly dangerous in multi-tenant Cassandra setups where data isolation relies entirely on correct partition key usage.

Another Cassandra-specific pattern involves secondary index abuse. When applications use secondary indexes for filtering without proper authorization checks, attackers can bypass intended data access controls:

SELECT * FROM patient_records WHERE doctor_id = ? AND appointment_date = ?;

Here, an attacker modifies the doctor_id parameter to access appointments belonging to other doctors, potentially exposing patient-doctor relationships and medical information.

Cassandra's lightweight transactions (LWT) can also introduce BOLA vulnerabilities. When applications use LWT for optimistic concurrency control but fail to verify row ownership:

SELECT * FROM user_preferences WHERE user_id = ? AND preference_key = ?;

An attacker could modify the user_id parameter to overwrite preferences for other users, leading to privilege escalation or data corruption.

Time-series data in Cassandra presents unique BOLA challenges. Applications often use time-based partitioning without proper user scoping:

SELECT * FROM user_activity WHERE user_id = ? AND bucket_time = ?;

If the application doesn't validate that the requesting user owns the data in the specified time bucket, attackers can enumerate historical activity data across users.

Detecting BOLA/IdOR in Cassandra requires understanding both the data model and query patterns. middleBrick's Cassandra-specific scanning identifies these vulnerabilities through several techniques:

Partition key analysis examines how applications construct CQL queries and whether they properly validate user permissions against partition key values. The scanner looks for patterns where user input directly maps to partition key components without authorization checks.

Secondary index scanning identifies queries that use indexed columns for access control rather than partition keys. This is particularly important in Cassandra since secondary indexes can create unexpected query paths that bypass primary data access controls.

middleBrick tests for BOLA by systematically modifying partition key values in authenticated sessions and observing whether the application returns data for modified keys. For example, if a user can access their own records at patient_id = '123', the scanner tests whether patient_id = '124' returns data for another user.

The scanner also examines lightweight transaction patterns, testing whether LWT operations properly scope to the authenticated user's data. This includes checking both read-modify-write cycles and conditional updates.

For time-series data, middleBrick analyzes bucket size and access patterns to identify whether users can enumerate data across time boundaries or access other users' historical data by manipulating timestamp parameters.

middleBrick's LLM security features add another layer of detection for Cassandra applications using AI/ML components. The scanner tests for system prompt leakage in applications that use Cassandra as a vector for AI-powered features, ensuring that database access patterns don't expose sensitive configuration or data through AI interfaces.

Real-world example: A financial services application using Cassandra for transaction storage had a BOLA vulnerability where users could access other accounts' transaction history by modifying account_id in query parameters. middleBrick detected this by successfully retrieving transactions for accounts the authenticated user didn't own.

Remediating BOLA/IdOR in Cassandra requires a defense-in-depth approach that combines proper data modeling, query patterns, and application-level authorization checks.

Primary key design is the first line of defense. Structure your partition keys to include user identifiers or tenant IDs as the first component:

CREATE TABLE patient_records (  hospital_id text,  patient_id text,  record_type text,  data blob,  PRIMARY KEY ((hospital_id, patient_id), record_type) );

This design ensures that queries must include both the hospital and patient identifiers, making it harder to accidentally access other users' data through partition key manipulation.

Application-level authorization should validate that the authenticated user has rights to the data being accessed. Implement a centralized authorization layer that checks permissions before executing any CQL query:

function getPatientRecord(hospitalId, patientId, recordType, userId) {  // Verify user has access to this hospital  if (!userHasHospitalAccess(userId, hospitalId)) {    throw new Error('Unauthorized access');  }  // Verify user has access to this patient  if (!userHasPatientAccess(userId, patientId)) {    throw new Error('Unauthorized access');  }  // Now execute the query  const query = 'SELECT * FROM patient_records WHERE hospital_id = ? AND patient_id = ? AND record_type = ?';  return cassandraClient.execute(query, [hospitalId, patientId, recordType]);}

Secondary index usage requires careful consideration. Instead of using secondary indexes for access control, use materialized views or denormalized data structures that maintain proper user scoping:

CREATE MATERIALIZED VIEW doctor_appointments_by_doctor AS  SELECT * FROM appointments  WHERE doctor_id IS NOT NULL AND appointment_id IS NOT NULL  PRIMARY KEY ((doctor_id), appointment_id);

This ensures that queries against the materialized view are naturally scoped to the doctor's data.

For lightweight transactions, always verify row ownership before performing conditional updates. Use application-level checks combined with database constraints:

async function updatePreference(userId, preferenceKey, newValue) {  // Verify row exists and belongs to user  const existing = await getPreference(userId, preferenceKey);  if (!existing || existing.user_id !== userId) {    throw new Error('Unauthorized access');  }  // Perform LWT with proper validation  const query = 'UPDATE user_preferences SET value = ? WHERE user_id = ? AND preference_key = ? IF EXISTS';  const result = await cassandraClient.execute(query, [newValue, userId, preferenceKey]);  if (!result.wasApplied()) {    throw new Error('Update conflict or unauthorized');  }}

Time-series data should use user-scoped partitioning strategies. Include user identifiers in time bucket keys to prevent cross-user data enumeration:

CREATE TABLE user_activity (  user_id text,  bucket_time timestamp,  activity_type text,  activity_data text,  PRIMARY KEY ((user_id, bucket_time), activity_type) );

This design ensures that time-based queries are automatically scoped to the authenticated user's data.

middleBrick's continuous monitoring in Pro tier can help verify that remediation efforts are effective by regularly scanning your Cassandra APIs and alerting on any regression in BOLA protections.