sec-design-deep-analysis.md

Deep Security Analysis of Cube.js

1. Objective, Scope, and Methodology

Objective:

The objective of this deep security analysis is to conduct a thorough examination of the key components of the Cube.js framework, identify potential security vulnerabilities and weaknesses, and provide actionable mitigation strategies. The analysis will focus on the core components identified in the security design review, including the API, Schema Compiler, Query Orchestrator, Connection Manager, and Data Schema, as well as the build and deployment processes. The goal is to ensure that Cube.js deployments are secure by design and that appropriate security controls are in place to protect against common threats.

Scope:

This analysis covers the following aspects of Cube.js:

• Core Components: API, Schema Compiler, Query Orchestrator, Connection Manager, Data Schema.
• Deployment: Kubernetes-based deployment (as chosen in the design review).
• Build Process: CI/CD pipeline, dependency management, and containerization.
• Security Controls: Authentication, authorization, input validation, SQL injection prevention, CORS, rate limiting, auditing, and logging.
• Data Flow: Analysis of how data flows through the system and potential points of vulnerability.
• Threat Model: Identification of potential threats and attack vectors.

This analysis does not cover:

• Security of external data sources connected to Cube.js (this is the responsibility of the user).
• Security of the underlying Kubernetes infrastructure (this is the responsibility of the user).
• Security of client-side applications consuming the Cube.js API (this is the responsibility of the user).
• Specific compliance requirements (e.g., GDPR, HIPAA) beyond general best practices. These need to be addressed on a case-by-case basis.

Methodology:

1. Architecture and Data Flow Inference: Based on the provided documentation, codebase snippets, and C4 diagrams, we will infer the detailed architecture and data flow within Cube.js.
2. Component-Specific Security Analysis: Each key component will be analyzed for potential security vulnerabilities, considering its specific responsibilities and interactions.
3. Threat Modeling: We will identify potential threats and attack vectors based on the architecture, data flow, and known vulnerabilities.
4. Mitigation Strategy Recommendation: For each identified vulnerability, we will provide specific, actionable, and tailored mitigation strategies.
5. Review of Existing Security Controls: We will assess the effectiveness of the existing security controls identified in the security design review.

2. Security Implications of Key Components

Let's break down the security implications of each key component:

2.1 API (Container)

• Responsibilities: Handles API requests, authentication, authorization, routing, input validation.
• Security Implications:
  • Authentication Bypass: Vulnerabilities in JWT validation or custom authentication implementations could allow attackers to bypass authentication. Improperly configured API keys or secrets could be leaked or misused.
  • Authorization Bypass: Flaws in the authorization logic could allow users to access data they are not permitted to see. This is particularly critical given the schema-based authorization mechanism.
  • Injection Attacks: Insufficient input validation could lead to various injection attacks, including:
    • Parameter Tampering: Modifying API parameters to manipulate query logic or access unauthorized data.
    • Cross-Site Scripting (XSS): If Cube.js echoes user input without proper sanitization (e.g., in error messages), XSS attacks might be possible. This is less likely in a backend API, but still a consideration.
    • NoSQL Injection: If a NoSQL database is used as a data source, insufficient validation of query parameters could lead to NoSQL injection attacks.
  • Denial of Service (DoS): Lack of rate limiting or resource limits could allow attackers to overwhelm the API with requests, making it unavailable to legitimate users.
  • CORS Misconfiguration: Overly permissive CORS settings could allow malicious websites to make unauthorized requests to the Cube.js API.
  • Sensitive Data Exposure: Error messages or API responses might inadvertently expose sensitive information about the system or data.
  • Lack of Auditing: Without proper auditing, it's difficult to track API usage, identify suspicious activity, and investigate security incidents.

2.2 Schema Compiler (Container)

• Responsibilities: Parses schema definitions, checks for errors, generates metadata.
• Security Implications:
  • Schema Injection: Maliciously crafted schema definitions could be injected to:
    • Bypass Authorization: Define overly permissive access rules.
    • Cause Denial of Service: Create computationally expensive schemas that overload the system.
    • Execute Arbitrary Code: Potentially exploit vulnerabilities in the schema parsing logic to execute arbitrary code (less likely, but a high-impact vulnerability).
  • Insecure Defaults: If the Schema Compiler allows insecure default settings, deployments might be vulnerable without explicit configuration changes.
  • Lack of Schema Validation: Insufficient validation of schema definitions could lead to unexpected behavior or vulnerabilities in other components.

2.3 Query Orchestrator (Container)

• Responsibilities: Plans query execution, coordinates with Connection Manager and Caching Layer, performs authorization checks.
• Security Implications:
  • Authorization Bypass: Errors in the authorization logic could allow unauthorized data access. This is a critical component for enforcing data access policies.
  • Query Manipulation: Attackers might try to manipulate the query execution plan to access unauthorized data or cause performance issues.
  • Resource Exhaustion: Complex or poorly optimized queries could consume excessive resources, leading to denial of service.
  • Cache Poisoning: If the caching layer is not properly secured, attackers might be able to inject malicious data into the cache, affecting subsequent queries.

2.4 Connection Manager (Container)

• Responsibilities: Establishes and manages database connections, executes queries.
• Security Implications:
  • SQL Injection: This is the most critical vulnerability for this component. Even with parameterized queries, subtle errors in how they are used could lead to SQL injection. Different database drivers might have different levels of protection.
  • Database Credential Exposure: If database credentials are not securely stored and managed, they could be compromised.
  • Connection String Injection: If the connection string is constructed from user input without proper validation, attackers could inject malicious parameters.
  • Man-in-the-Middle (MitM) Attacks: If connections to the database are not encrypted, attackers could intercept and modify data in transit.

2.5 Data Schema (Data)

• Responsibilities: Provides metadata for query planning and authorization.
• Security Implications:
  • Incorrect Access Control Rules: The security of the entire system relies on the correctness of the data schema. Errors in defining access control rules can lead to data breaches.
  • Data Leakage: The schema itself might inadvertently expose information about the underlying data structure or relationships.
  • Schema Tampering: If attackers can modify the schema, they can potentially gain unauthorized access to data.

3. Inferred Architecture and Data Flow

Based on the C4 diagrams and component descriptions, we can infer the following data flow:

1. Request Initiation: A user interacts with a client application, which sends an API request to the Cube.js API.
2. Authentication and Authorization (API): The API authenticates the user (e.g., using JWT) and performs initial authorization checks.
3. Schema Compilation (Schema Compiler): If the schema is not already cached, the API forwards the request to the Schema Compiler to compile and validate the relevant data schema.
4. Query Planning (Query Orchestrator): The API passes the request and compiled schema to the Query Orchestrator. The Query Orchestrator determines the optimal query execution plan, considering caching and data source availability. It also performs authorization checks based on the schema's access control rules.
5. Database Connection (Connection Manager): The Query Orchestrator uses the Connection Manager to establish a connection to the appropriate data source.
6. Query Execution (Connection Manager & Database): The Connection Manager executes the query against the database using parameterized queries.
7. Result Retrieval (Connection Manager & Database): The database returns the query results to the Connection Manager.
8. Caching (Caching Layer): The Query Orchestrator may cache the results in the caching layer (e.g., Redis).
9. Response Formatting (API): The API receives the results from the Query Orchestrator (either from the database or the cache) and formats them into the appropriate response format (e.g., JSON).
10. Response Delivery (API): The API sends the response back to the client application.

4. Tailored Mitigation Strategies

Here are specific, actionable, and tailored mitigation strategies for the identified vulnerabilities:

4.1 API Component:

• Authentication:
  • Strong JWT Validation: Implement robust JWT validation, including:
    • Signature Verification: Always verify the JWT signature using the correct secret key.
    • Expiration Check: Enforce token expiration.
    • Audience and Issuer Validation: Validate the aud (audience) and iss (issuer) claims.
    • "None" Algorithm Prevention: Explicitly reject JWTs with the "none" algorithm.
    • Key Rotation: Implement a secure key rotation mechanism for JWT secrets.
  • API Key Management:
    • Secure Storage: Store API keys securely (e.g., using environment variables, secrets management services). Never hardcode keys in the codebase.
    • Regular Rotation: Rotate API keys regularly.
    • Least Privilege: Assign API keys with the minimum necessary permissions.
  • Custom Authentication: If custom authentication is used, thoroughly vet the implementation for vulnerabilities. Consider using established authentication libraries.
• Authorization:
  • Schema-Based Authorization Auditing: Regularly audit the data schema to ensure that access control rules are correctly defined and enforced. Use automated tools to help identify potential issues.
  • Principle of Least Privilege: Grant users and roles the minimum necessary permissions to access data.
  • Dynamic Authorization Rules: Implement dynamic authorization rules based on user attributes or context, where appropriate.
• Input Validation:
  • Strict Input Validation: Validate all user inputs (query parameters, headers, request body) against a strict whitelist of allowed characters and formats. Use a well-vetted input validation library.
  • Parameter Type Enforcement: Enforce the correct data types for all parameters (e.g., numbers, strings, dates).
  • Regular Expression Validation: Use regular expressions to validate input formats, but be cautious of ReDoS (Regular Expression Denial of Service) vulnerabilities. Test regular expressions thoroughly.
  • NoSQL Injection Prevention: If using a NoSQL database, use a database driver or ORM that provides built-in protection against NoSQL injection. Sanitize all user inputs used in queries.
• Denial of Service (DoS) Protection:
  • Rate Limiting: Implement rate limiting at the API level (or using a reverse proxy like Nginx or HAProxy) to prevent attackers from overwhelming the system. Consider different rate limits for different API endpoints or user roles.
  • Resource Limits: Set resource limits (CPU, memory) for Cube.js processes to prevent resource exhaustion. Use Kubernetes resource quotas and limits.
  • Timeout Configuration: Configure appropriate timeouts for API requests to prevent long-running requests from tying up resources.
• CORS Configuration:
  • Restrictive CORS: Configure CORS settings to allow requests only from trusted origins. Avoid using wildcard origins (*) in production.
• Sensitive Data Exposure:
  • Generic Error Messages: Return generic error messages to users. Avoid exposing internal details or stack traces.
  • Logging: Log detailed error information, but ensure that logs are securely stored and protected from unauthorized access.
• Auditing:
  • Comprehensive Auditing: Implement comprehensive auditing to track all API requests, including:
    • Timestamp
    • User ID (if authenticated)
    • Client IP address
    • Request method and path
    • Request parameters
    • Response status code
    • Response time
  • Audit Log Security: Securely store audit logs and protect them from tampering. Consider using a centralized logging system.

4.2 Schema Compiler Component:

• Schema Injection Prevention:
  • Strict Schema Validation: Implement strict validation of schema definitions against a predefined schema. Reject any schema that does not conform to the expected structure.
  • Input Sanitization: Sanitize all inputs used in schema definitions to prevent injection attacks.
  • Sandboxing: Consider running the Schema Compiler in a sandboxed environment to limit the impact of potential vulnerabilities.
• Secure Defaults: Ensure that the Schema Compiler uses secure default settings. Require explicit configuration for any potentially insecure options.
• Regular Audits: Regularly audit the schema compiler's code and configuration for security vulnerabilities.

4.3 Query Orchestrator Component:

• Authorization Enforcement:
  • Robust Authorization Checks: Implement robust authorization checks based on the compiled data schema. Ensure that these checks are performed before any data is accessed.
  • Regular Audits: Regularly audit the authorization logic to ensure that it is correctly enforcing access control rules.
• Query Manipulation Prevention:
  • Query Rewriting: Consider using query rewriting techniques to prevent attackers from manipulating the query execution plan.
  • Input Validation: Validate all inputs used in query planning to prevent injection attacks.
• Resource Exhaustion Prevention:
  • Query Complexity Limits: Set limits on the complexity of queries that can be executed. Reject queries that are too complex or resource-intensive.
  • Resource Monitoring: Monitor resource usage (CPU, memory, database connections) to detect and prevent resource exhaustion attacks.
• Cache Poisoning Prevention:
  • Secure Cache Access: Restrict access to the caching layer to authorized components only.
  • Cache Key Validation: Validate cache keys to prevent attackers from injecting malicious data into the cache.
  • Cache Invalidation: Implement a robust cache invalidation mechanism to ensure that stale or malicious data is not served from the cache.

4.4 Connection Manager Component:

• SQL Injection Prevention:
  • Parameterized Queries: Always use parameterized queries or prepared statements to interact with databases. Never construct SQL queries by concatenating user input.
  • Database Driver Security: Use a database driver that provides built-in protection against SQL injection. Keep the driver up to date.
  • Least Privilege (Database): Grant the database user used by Cube.js the minimum necessary privileges. Avoid using database superuser accounts.
  • Regular Code Reviews: Regularly review the code that interacts with the database to ensure that parameterized queries are being used correctly.
  • Static Analysis: Use static analysis tools to scan for potential SQL injection vulnerabilities.
• Database Credential Management:
  • Secure Storage: Store database credentials securely (e.g., using environment variables, secrets management services, Kubernetes secrets). Never hardcode credentials in the codebase.
  • Regular Rotation: Rotate database credentials regularly.
• Connection String Injection Prevention:
  • Input Validation: Validate all inputs used to construct connection strings. Use a whitelist of allowed characters and formats.
• Man-in-the-Middle (MitM) Attack Prevention:
  • Encrypted Connections: Use encrypted connections (TLS/SSL) to connect to the database. Enforce TLS/SSL and disable insecure protocols.
  • Certificate Validation: Validate the database server's certificate to ensure that you are connecting to the legitimate server.

4.5 Data Schema:

• Access Control Rule Auditing:
  • Regular Reviews: Regularly review and audit the data schema to ensure that access control rules are correctly defined and enforced.
  • Automated Tools: Use automated tools to help identify potential issues with access control rules.
• Data Leakage Prevention:
  • Schema Minimization: Include only the necessary information in the data schema. Avoid exposing sensitive details about the underlying data structure.
• Schema Tampering Prevention:
  • Access Control: Restrict access to the data schema to authorized users and components only.
  • Integrity Checks: Implement integrity checks to detect unauthorized modifications to the data schema.

5. Review of Existing Security Controls

The security design review identified several existing security controls. Here's an assessment:

• Authentication (JWT, Custom): Good foundation, but needs robust validation and key management (as detailed above).
• Authorization (Schema-based): Powerful, but requires careful configuration and auditing. The principle of least privilege is crucial.
• API Key Management: Needs secure storage, rotation, and least privilege implementation.
• SQL Injection Prevention (Parameterized Queries): Essential, but requires rigorous adherence and code review. Different database drivers may have different nuances.
• CORS: Needs to be configured restrictively, avoiding wildcards in production.
• Rate Limiting (Cube.js Cloud, Infrastructure-level): Good, but self-hosted deployments need explicit configuration (e.g., using Nginx).
• Input Validation (Schema Definitions): Needs to be expanded to cover all user inputs, not just schema definitions.
• Dependency Management (npm/yarn): Good practice, but requires ongoing monitoring for security advisories and timely updates.

The "Accepted Risks" are reasonable, but highlight the importance of providing clear security documentation and best practices for users.

The "Recommended Security Controls" are all valuable additions. Auditing and logging are particularly important for detecting and investigating security incidents. Encryption at rest and in transit should be prioritized for sensitive data.

The "Security Requirements" provide a good high-level overview. The emphasis on strong cryptography and secure key management is crucial.

Conclusion

Cube.js has a solid foundation for security, but requires careful configuration and ongoing vigilance. The most critical areas to focus on are:

• SQL Injection Prevention: This is the most likely and highest-impact vulnerability.
• Authorization Enforcement: The schema-based authorization mechanism is powerful, but needs to be carefully configured and audited.
• Input Validation: Strict input validation is essential to prevent a wide range of attacks.
• Secure Credential Management: Protecting API keys, JWT secrets, and database credentials is crucial.
• Auditing and Logging: Comprehensive auditing is essential for detecting and investigating security incidents.

By implementing the mitigation strategies outlined above, Cube.js deployments can be significantly hardened against potential threats. Regular security assessments and penetration testing are also recommended to identify and address any remaining vulnerabilities.