Insecure Design in Cassandra

Insecure Design in Cassandra manifests through architectural decisions that expose data or functionality beyond intended boundaries. Unlike implementation bugs, these are design flaws where the system's structure itself creates security vulnerabilities.

The most critical manifestation occurs in Cassandra's role-based access control (RBAC) system. By default, Cassandra creates a superuser role during initialization, but many deployments fail to properly restrict this role's permissions. An insecure design pattern emerges when developers grant broad ALL PERMISSIONS to application roles without implementing least-privilege principles:

// Insecure design - overly permissive role creation
CREATE ROLE my_app_user WITH PASSWORD 'password' AND LOGIN = true;
GRANT ALL PERMISSIONS ON KEYSPACE my_keyspace TO my_app_user;

This design flaw allows any compromised application credential to access all data within the keyspace, including sensitive columns that should remain private. The problem compounds when developers create data models without considering column-level security, leading to scenarios where a single role can read or modify all rows regardless of data sensitivity.

Another Cassandra-specific insecure design pattern involves improper handling of materialized views and secondary indexes. Developers often create views without considering the security implications of data duplication. A materialized view inherits the base table's permissions, but if the base table grants broad access, the view exposes the same data through a different query path:

// Insecure design - materialized view exposes sensitive data
CREATE MATERIALIZED VIEW user_emails AS
SELECT email, user_id, last_login
FROM users
WHERE email IS NOT NULL AND user_id IS NOT NULL
PRIMARY KEY (email, user_id);

This design allows any user with read access to the users table to query emails through the materialized view, even if email access should be restricted to specific roles.

Time-to-live (TTL) settings represent another insecure design vector. Developers often set uniform TTL values across all data types, failing to consider that some data requires longer retention while other data should expire quickly. This uniform approach creates compliance risks and potential data exposure:

// Insecure design - uniform TTL ignores data sensitivity
CREATE TABLE session_data (
    session_id text,
    user_data text,
    sensitive_data text,
    PRIMARY KEY (session_id)
) WITH default_time_to_live = 86400;

Here, sensitive data shares the same expiration policy as non-sensitive session metadata, creating potential retention compliance violations.

Detecting insecure design in Cassandra requires both configuration analysis and runtime scanning. The most effective approach combines schema inspection with permission auditing to identify design flaws before they're exploited.

Permission analysis reveals the most common insecure designs. Using Cassandra's system tables, you can identify overly permissive role assignments:

// Detection query - find overly permissive roles
SELECT role, resource, permission
FROM system_auth.role_permissions
WHERE resource LIKE 'data%' OR resource LIKE 'roles%'
ORDER BY role;

This query exposes roles with broad ALL PERMISSIONS grants, which represent insecure design choices. The output helps identify where least-privilege principles were violated during role creation.

Schema analysis detects insecure data modeling patterns. Materialized views that duplicate sensitive data without proper access controls indicate design flaws:

// Detection query - find materialized views with sensitive columns
SELECT *
FROM system_schema.views
WHERE view_name LIKE '%sensitive%'
OR view_definition LIKE '%email%' OR view_definition LIKE '%password%';

This identifies views that potentially expose sensitive columns through alternative query paths, a classic insecure design pattern.

middleBrick's black-box scanning approach detects insecure design by testing the API surface exposed by Cassandra drivers and applications. The scanner identifies endpoints that allow unauthorized data access through BOLA (Broken Object Level Authorization) testing:

// Example of BOLA vulnerability middleBrick detects
// Insecure endpoint allowing user ID manipulation
GET /api/users/123/data
// Should only access user 123's data, but returns all users' data

The scanner tests for improper authorization checks by manipulating identifiers in API requests, revealing whether the backend properly enforces data access boundaries.

middleBrick's LLM security features are particularly relevant for Cassandra deployments using AI/ML features. The scanner detects if LLM endpoints are exposed without authentication, which could allow attackers to extract system prompts or training data stored in Cassandra:

// middleBrick detects unauthenticated LLM endpoints
// Example finding: LLM endpoint accessible without API key
GET /v1/chat/completions
Status: 200 OK (unauthenticated)

This detection is crucial as more Cassandra deployments integrate with AI services that store prompt data or training datasets in the database.

Remediating insecure design in Cassandra requires architectural changes rather than simple configuration updates. The most effective approach implements defense-in-depth principles throughout the data model and access control layers.

Role-based access control should follow least-privilege principles with granular permissions. Instead of granting ALL PERMISSIONS, create roles with specific, minimal permissions:

// Secure design - least-privilege role creation
CREATE ROLE reporting_user WITH PASSWORD 'secure_pass' AND LOGIN = true;
GRANT SELECT ON my_keyspace.sales_data TO reporting_user;
GRANT EXECUTE ON my_keyspace.sales_procedures TO reporting_user;

This design ensures the reporting user can only read sales data and execute specific procedures, preventing access to other keyspace contents.

Data modeling should incorporate security considerations from the start. Use separate tables or column families for sensitive data, with distinct access controls:

// Secure design - separate sensitive data
CREATE TABLE public_user_data (
    user_id text,
    username text,
    email text,
    PRIMARY KEY (user_id)
);

CREATE TABLE sensitive_user_data (
    user_id text,
    ssn text,
    financial_info text,
    PRIMARY KEY (user_id)
) WITH additional_write_policy = 'NONE';

// Grant different permissions to different roles
GRANT SELECT ON my_keyspace.public_user_data TO public_user;
GRANT SELECT ON my_keyspace.sensitive_user_data TO sensitive_access_user;

This separation ensures that even if one role is compromised, sensitive data remains protected by distinct access controls.

Materialized views require careful security planning. Only create views when necessary, and ensure they don't expose data through unintended query paths:

// Secure design - controlled materialized views
CREATE MATERIALIZED VIEW IF NOT EXISTS active_users AS
SELECT user_id, username, last_active
FROM users
WHERE user_id IS NOT NULL AND last_active IS NOT NULL
PRIMARY KEY (last_active, user_id)
WITH read_repair_chance = 0.0;

// Restrict view access
REVOKE ALL PERMISSIONS ON my_keyspace.active_users FROM public_user;
GRANT SELECT ON my_keyspace.active_users TO reporting_user;

This design ensures only authorized reporting roles can access the materialized view, preventing unauthorized data discovery.

TTL policies should be data-classification aware, with different expiration policies based on data sensitivity:

// Secure design - data-classification aware TTL
CREATE TABLE session_data (
    session_id text,
    user_data text static,
    sensitive_data text,
    PRIMARY KEY (session_id)
) WITH default_time_to_live = 86400;

CREATE TABLE pii_data (
    user_id text,
    ssn text,
    financial_info text,
    PRIMARY KEY (user_id)
) WITH default_time_to_live = 0; -- Never expire, manual cleanup only

This approach ensures sensitive PII data remains until explicitly removed, while session data expires automatically, reducing the attack surface.

middleBrick's continuous monitoring helps verify that remediation efforts remain effective over time. The Pro plan's scheduled scans can detect when new insecure designs are introduced during development:

# middleBrick CLI - schedule regular scans
middlebrick scan https://api.myapp.com --schedule=daily --threshold=B

# GitHub Action - fail builds on insecure design
- name: middleBrick Security Scan
  uses: middlebrick/middlebrick-action@v1
  with:
    api_url: ${{ secrets.API_URL }}
    fail_threshold: B

These integrations ensure that insecure design patterns are caught early in the development lifecycle, before reaching production.