mitigations.md

Mitigation Strategies Analysis for perwendel/spark

Mitigation Strategy: 1. Mitigation Strategy: Explicit and Validated Route Management (Spark API)

• Description:

  1. Centralize Route Definitions: Use Spark's Spark.get(), Spark.post(), Spark.put(), etc., methods within a single, dedicated file or class (e.g., Routes.java).
  2. Strict Ordering: Define routes using Spark's methods in a deterministic order, placing more specific routes before more general ones. This directly utilizes Spark's route matching logic.

     Spark.get("/users/profile", ...); // More specific
     Spark.get("/users/:id", ...);     // Less specific

  3. Route Validation (if dynamic, using Spark API): If routes are loaded dynamically:
     • a) Overlap Check: Before adding a new route using Spark.get() (or similar), programmatically check existing routes (potentially stored in a list or map) for overlaps. This would involve custom logic before calling the Spark API.
     • b) Pattern Validation: Use regular expressions to validate the format of new routes before passing them to Spark.get().
     • c) Authorization Check: Verify permissions before calling Spark.get().
  4. Avoid Wildcard Abuse: Minimize the use of wildcards (*) in Spark route definitions (Spark.get("/users/*", ...)). Use path parameters (Spark.get("/users/:id", ...)) whenever possible.
  5. Route Listing (for Auditing): While Spark doesn't have a built-in route listing API, you can build one. Maintain a list of routes as you define them using Spark.get(), etc., and create an endpoint that exposes this list (for internal use/auditing only).

• Threats Mitigated:

  • Route Hijacking (High Severity): Prevents attackers from defining routes that intercept legitimate requests using Spark's routing mechanism.
  • Unintended Route Exposure (Medium Severity): Reduces the risk of exposing sensitive functionality.
  • Regular Expression Denial of Service (DoS) in Routes (Medium Severity): Validation before calling Spark's methods prevents malicious regex.

• Impact:

  • Route Hijacking: Risk significantly reduced.
  • Unintended Route Exposure: Risk significantly reduced.
  • Regular Expression DoS: Risk significantly reduced.

• Currently Implemented:

  • Centralized route definitions using Spark.get(), etc.
  • Some basic ordering.

• Missing Implementation:

  • Comprehensive route validation logic before calling Spark's route definition methods.
  • Stricter enforcement of wildcard usage within Spark route definitions.
  • A custom route listing endpoint (for auditing).

Mitigation Strategy: 2. Mitigation Strategy: Secure Filter Configuration and Ordering (Spark API)

• Description:

  1. Centralized Filter Management: Define all filters using Spark.before() and Spark.after() in a well-defined location.
  2. Strict Filter Ordering: Use Spark.before() in the correct order to ensure security-critical filters execute first:

     Spark.before("/api/*", authenticationFilter); // Authentication first, using Spark.before()
     Spark.before("/api/*", authorizationFilter);  // Then authorization

  3. Path Specificity: Use specific paths with Spark.before() and Spark.after():

     Spark.before("/admin/*", adminAuthFilter); // Specific path
     // Avoid: Spark.before("/*", adminAuthFilter); // Unless truly global

  4. Global Filters (with Caution): Use Spark.before("/*", ...) and Spark.after("/*", ...) only when absolutely necessary for security-critical checks.
  5. halt() Usage: Use Spark.halt() within filters to stop request processing, setting appropriate status codes and messages:

     Spark.before("/protected/*", (request, response) -> {
         if (!isAuthenticated(request)) {
             Spark.halt(401, "Unauthorized"); // Using Spark.halt()
         }
     });

  6. after Filter Restrictions: In Spark.after() filters, avoid modifying the response body based on untrusted data.
  7. Filter Validation (if dynamic): If filters are loaded dynamically, validate their configuration before calling Spark.before() or Spark.after().

• Threats Mitigated:

  • Authentication Bypass (High Severity): Correct use of Spark.before() for authentication.
  • Authorization Bypass (High Severity): Correct use of Spark.before() for authorization.
  • Cross-Site Scripting (XSS) (High Severity): Input sanitization filters (using Spark.before()) can help.
  • Information Disclosure (Medium Severity): Proper Spark.halt() usage.

• Impact:

  • Authentication/Authorization Bypass: Risk significantly reduced.
  • XSS: Risk reduced (in conjunction with output encoding).
  • Information Disclosure: Risk reduced.

• Currently Implemented:

  • Basic filter ordering using Spark.before().
  • Spark.halt() used in some filters.

• Missing Implementation:

  • Comprehensive review and refactoring of Spark.before() and Spark.after() calls.
  • Stricter path specificity in Spark.before() and Spark.after() calls.
  • Consistent and secure Spark.halt() usage (review all calls).
  • Review of Spark.after() filter logic.
  • Dynamic filter validation (if applicable) before calling Spark's filter methods.

Mitigation Strategy: 3. Mitigation Strategy: Robust Exception Handling (Spark API)

• Description:

  1. Custom Exception Handlers: Use Spark.exception() to define custom exception handlers:

     Spark.exception(Exception.class, (exception, request, response) -> {
         // ... (logging, setting status code, generic error message) ...
     });

     This is directly using the Spark API.
  2. No Stack Traces in Production: Ensure that within the Spark.exception() handler, stack traces are not included in the response body sent to the user.
  3. Centralized Error Handling: Use Spark.exception() as the primary mechanism for handling exceptions, avoiding scattered try-catch blocks.
  4. Specific Exception Handling: Use specific exception types with Spark.exception():

     Spark.exception(NumberFormatException.class, (exception, request, response) -> {
         // Handle NumberFormatException specifically
     });

• Threats Mitigated:

  • Information Disclosure (Medium Severity): Prevents leaking sensitive information through exception messages via Spark's response handling.

• Impact:

  • Information Disclosure: Risk significantly reduced.

• Currently Implemented:

  • Likely relying on Spark's default exception handling.

• Missing Implementation:

  • Implementation of custom exception handlers using Spark.exception().
  • Centralized error handling using Spark.exception().
  • Ensuring no stack traces are sent in responses within the Spark.exception() handlers.