attack-surface.md

Attack Surface Analysis for perwendel/spark

Attack Surface: Overly Permissive Routes

• Description: Unintentionally exposing endpoints due to broad route definitions.
• Spark Contribution: Spark's concise routing syntax (e.g., get("/public/*", ...) ) makes it very easy to accidentally create overly broad routes that match more requests than intended. This is a direct consequence of Spark's design philosophy of simplicity.
• Example: A route defined as /api/* intended only for authenticated users might be accessible without authentication if a before filter is missing, misconfigured, or applied too late in the filter chain. An attacker could access /api/admin/users without credentials.
• Impact: Unauthorized access to sensitive data or functionality, potential privilege escalation.
• Risk Severity: High
• Mitigation Strategies:
  • Developers: Define routes with the most specific paths possible. Avoid wildcards (*) unless absolutely necessary and carefully controlled. Prefer /api/users/list over /api/users/* and definitely over /api/*.
  • Developers: Implement robust authentication and authorization before any route handling logic using before filters. These filters must be applied before the route handler is executed. Verify user roles and permissions within the filter.
  • Developers: Use a linter or static analysis tool that can be configured to flag overly permissive routes (e.g., routes that match too many potential paths).
  • Developers/Security Team: Conduct regular penetration testing specifically targeting route exposure, attempting to access endpoints without proper credentials.

Attack Surface: Missing or Incorrect before and after Filters (Specifically related to routing)

• Description: Failure to properly implement or configure before filters for authentication, authorization, and input validation directly tied to Spark's routing mechanism.
• Spark Contribution: Spark relies on before and after filters for essential security controls within its request handling pipeline. The framework provides these filters specifically for this purpose, and their misuse is a direct Spark-related vulnerability. This is different from general filter misuse; it's about how Spark expects security to be implemented.
• Example: A before filter intended to check user authentication is accidentally applied only to /api/data and not to /api/admin, leaving the admin endpoint unprotected. Or, a filter is defined but never actually added to the Spark application using Spark.before(...).
• Impact: Authentication bypass, authorization bypass, injection vulnerabilities (if input validation is done in a filter).
• Risk Severity: High
• Mitigation Strategies:
  • Developers: Implement robust before filters for authentication and authorization on all routes that require protection. Ensure these filters are applied before any route-specific logic.
  • Developers: Use before filters for input validation and sanitization specifically for data received through Spark routes. Reject or clean any potentially malicious input before it reaches the route handler.
  • Developers: Thoroughly test filter logic, including edge cases and bypass attempts. Use unit tests to verify that filters are applied to the correct routes and in the correct order. Test for filter ordering issues.
  • Developers: Ensure filters are applied in the correct order (e.g., authentication must happen before authorization). Spark executes filters in the order they are defined.
  • Developers: Use a consistent naming convention and clear documentation for filters to avoid confusion and ensure they are applied correctly.