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attack-surface.md

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Attack Surface Analysis for cube-js/cube

Attack Surface: 1. Unsecured GraphQL API Endpoint

  • Description: Cube.js exposes a GraphQL API endpoint (typically /graphql). If this endpoint is not protected by authentication, it's publicly accessible.
  • Cube.js Contribution: Cube.js's core functionality is to provide a GraphQL API for data access, inherently creating this endpoint.
  • Example: An attacker directly accesses /graphql without any login or API key and can query all exposed data models, potentially extracting sensitive business data.
  • Impact: Data breaches, unauthorized data extraction, potential denial of service.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Implement Authentication: Use Cube.js's built-in authentication mechanisms (e.g., JWT, API keys) or integrate with existing application authentication frameworks.
    • Restrict Access: Use network firewalls or web application firewalls (WAFs) to limit access to the /graphql endpoint to authorized sources and networks.
    • Regularly Review Access Controls: Ensure authentication and authorization rules are correctly configured and reviewed periodically to prevent misconfigurations.

Attack Surface: 2. Exposure of Sensitive Data in Data Models

  • Description: Data models defined in Cube.js might inadvertently expose sensitive data fields through the GraphQL API that should not be publicly accessible or accessible to all users.
  • Cube.js Contribution: Cube.js data models directly define what data is exposed and queryable through its API.
  • Example: A Cube.js data model includes a field containing personally identifiable information (PII) or confidential business metrics that is unintentionally exposed in the GraphQL schema and can be queried by unauthorized users.
  • Impact: Data breaches, privacy violations, regulatory non-compliance, reputational damage.
  • Risk Severity: High to Critical (depending on the sensitivity of exposed data)
  • Mitigation Strategies:
    • Data Model Review: Carefully review and audit data models to ensure only necessary and non-sensitive data is exposed through the API. Apply the principle of least privilege to data exposure.
    • Field-Level Authorization: Implement field-level authorization within Cube.js to control access to specific fields based on user roles or permissions, ensuring sensitive fields are protected.
    • Data Masking/Redaction: Consider masking or redacting sensitive data fields in the data model or during query processing if full access control is not feasible or as an additional layer of security.

Attack Surface: 3. Insecure Configuration of Data Sources

  • Description: Weak credentials, exposed connection strings, or unencrypted connections to underlying data sources used by Cube.js can be exploited to gain unauthorized access to sensitive data.
  • Cube.js Contribution: Cube.js relies on connecting to various data sources, and insecure configurations in these connections directly impact Cube.js application security.
  • Example: Database credentials for the data warehouse are hardcoded in Cube.js configuration files, stored in plain text in environment variables, or accessed over unencrypted connections. An attacker gains access to these credentials and compromises the database, potentially bypassing Cube.js entirely.
  • Impact: Data breaches, unauthorized access to backend systems, data manipulation, complete compromise of underlying data stores.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Secure Credential Management: Utilize secure credential management practices such as environment variables (managed securely), secrets management systems (e.g., HashiCorp Vault, AWS Secrets Manager), or configuration files with restricted file system permissions. Avoid hardcoding credentials.
    • Principle of Least Privilege: Grant only the minimum necessary database permissions to the Cube.js user connecting to the data sources.
    • Encrypted Connections: Enforce encrypted connections (e.g., TLS/SSL) for all data source connections to protect data in transit.
    • Regular Security Audits: Conduct regular security audits of data source configurations and access controls to identify and remediate potential vulnerabilities.

Attack Surface: 4. Vulnerabilities in Cube.js Dependencies

  • Description: Outdated or vulnerable dependencies used by Cube.js (Node.js packages) can contain known security flaws that attackers can exploit to compromise the Cube.js application and potentially the underlying server.
  • Cube.js Contribution: Cube.js, being a Node.js application, relies on a vast ecosystem of dependencies, inheriting the inherent risks of dependency vulnerabilities.
  • Example: A known remote code execution vulnerability is discovered in a popular Node.js library used by Cube.js. If the Cube.js application uses a vulnerable version of this library, an attacker can exploit this vulnerability to execute arbitrary code on the Cube.js server.
  • Impact: Remote code execution, denial of service, information disclosure, complete server compromise, lateral movement within the network.
  • Risk Severity: High to Critical (depending on the nature and exploitability of the vulnerability)
  • Mitigation Strategies:
    • Dependency Scanning: Implement automated dependency scanning in the development pipeline and CI/CD process using tools like npm audit, yarn audit, or dedicated Software Composition Analysis (SCA) tools to identify known vulnerabilities.
    • Dependency Updates: Keep Cube.js and its dependencies updated to the latest versions, promptly applying security patches and updates released by the Cube.js team and dependency maintainers.
    • Software Composition Analysis (SCA): Integrate SCA tools for continuous monitoring of dependencies in production environments, providing alerts for newly discovered vulnerabilities.

Attack Surface: 5. Insufficient or Flawed Authorization Logic

  • Description: Even with authentication in place, if the authorization logic within Cube.js is not correctly implemented or contains flaws, attackers might be able to bypass authorization checks and access data or perform actions they are not permitted to.
  • Cube.js Contribution: Cube.js provides mechanisms for authorization, but the responsibility for implementing correct and robust authorization logic lies with the developers using Cube.js. Flaws in this implementation directly create vulnerabilities.
  • Example: Authorization rules are not correctly configured in Cube.js, allowing users with lower privileges to access data or perform actions intended only for administrators or users with higher roles. This could involve bypassing intended data model restrictions or query-level access controls.
  • Impact: Unauthorized data access, privilege escalation, data breaches, data manipulation, unauthorized modification of system configurations.
  • Risk Severity: High
  • Mitigation Strategies:
    • Robust Authorization Implementation: Implement robust and well-tested authorization logic in Cube.js, carefully considering different levels of access control (e.g., data model, query, field level) and user roles/permissions. Follow the principle of least privilege.
    • Principle of Least Privilege: Grant users and roles only the minimum necessary permissions required to perform their intended tasks within the Cube.js application.
    • Authorization Testing: Thoroughly test authorization logic with various user roles and scenarios, including negative testing to ensure it functions as intended and effectively prevents unauthorized access.
    • Regular Security Reviews: Conduct regular security reviews of authorization configurations and code to identify and address any potential flaws or misconfigurations.

Attack Surface: 6. Misconfigured Pre-aggregations

  • Description: Misconfigured pre-aggregations in Cube.js can inadvertently expose aggregated data in ways not intended or bypass intended access controls, potentially leading to unauthorized data access.
  • Cube.js Contribution: Cube.js's pre-aggregation feature, if not configured with security in mind, can introduce vulnerabilities related to data exposure and access control bypass.
  • Example: A pre-aggregation is created that aggregates sensitive data without properly applying the same filters or authorization rules that are enforced on the raw data. This allows users to access aggregated data they should not be able to see at a granular level, or access aggregated data that reveals sensitive patterns not intended for their access level.
  • Impact: Data breaches, unauthorized data access, potential for data manipulation if pre-aggregation logic is flawed.
  • Risk Severity: High (depending on the sensitivity of the data exposed through misconfigured pre-aggregations)
  • Mitigation Strategies:
    • Pre-aggregation Review: Carefully review pre-aggregation definitions to ensure they strictly adhere to intended access controls and data security policies. Verify that pre-aggregations do not inadvertently bypass authorization rules.
    • Authorization in Pre-aggregations: Apply authorization logic within pre-aggregation definitions to filter and aggregate data based on user permissions, mirroring the authorization applied to raw data queries.
    • Regular Monitoring: Monitor pre-aggregation jobs and data access patterns to detect any anomalies or unintended data exposure resulting from misconfigurations. Regularly audit pre-aggregation configurations.