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threat-modeling.md

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Threat Model Analysis for perwendel/spark

Threat: Route Manipulation

  • Threat: Route Manipulation
  • Description: An attacker gains access to the codebase or configuration files and modifies existing route definitions or injects new malicious routes within Spark's routing mechanism. They might add routes that expose sensitive data, execute arbitrary code, or redirect users to phishing sites. This is direct manipulation of Spark's routing.
  • Impact: Unauthorized access to data, arbitrary code execution, application compromise, user redirection to malicious sites.
  • Spark Component Affected: Route definitions (e.g., get(), post(), put(), etc.), Spark.routes(), configuration files specifically related to Spark's routing.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Implement strict access controls to the codebase and configuration files.
    • Use a secure development lifecycle (SDLC) with code reviews and least privilege principles.
    • Store route configurations securely (e.g., environment variables, secure configuration management system). Ensure these are not readable by the application itself after startup.
    • Regularly audit route definitions for unauthorized changes.

Threat: Filter Bypass/Injection (Spark-Specific Logic)

  • Threat: Filter Bypass/Injection (Spark-Specific Logic)
  • Description: An attacker exploits vulnerabilities in Spark's before or after filters to inject malicious code or bypass security checks specifically implemented within these Spark filters. This focuses on the Spark-specific logic within the filters, not general filter vulnerabilities. The vulnerability lies in how Spark uses the filters, and how the developer implements them within Spark.
  • Impact: Bypass of Spark-level security controls (authentication, authorization), arbitrary code execution within the Spark context, application compromise.
  • Spark Component Affected: before() and after() filters as used by Spark.
  • Risk Severity: High
  • Mitigation Strategies:
    • Treat filter code with the same level of security scrutiny as route handler code. Focus on the Spark-specific logic.
    • Avoid using untrusted input within filters in ways that affect Spark's behavior.
    • Thoroughly review filter code for potential vulnerabilities, especially related to how they interact with Spark's request/response cycle.
    • Implement centralized authorization checks before filters, if possible, within Spark's control.

Threat: Authorization Bypass via Spark Routing

  • Threat: Authorization Bypass via Spark Routing
  • Description: An attacker crafts requests that manipulate route parameters or exploit flaws in authorization logic implemented within Spark route handlers or Spark filters to gain unauthorized access. This is about bypassing authorization as implemented within Spark's control.
  • Impact: Unauthorized access to data or functionality, privilege escalation within the application context defined by Spark.
  • Spark Component Affected: Route handlers, before filters (if authorization is implemented there) - specifically the Spark-controlled parts.
  • Risk Severity: High
  • Mitigation Strategies:
    • Implement centralized authorization checks before route handler logic within Spark's control (e.g., using before filters or a dedicated middleware integrated with Spark).
    • Enforce the principle of least privilege within the Spark application.
    • Thoroughly test authorization logic for bypass vulnerabilities, focusing on how it interacts with Spark's routing.
    • Use a well-vetted authorization library if it integrates well with Spark's lifecycle.

Threat: Malicious Response Modification (Direct Spark Control)

  • **Threat:** Malicious `Response` Modification (Direct Spark Control)

    • **Description:**  An attacker exploits vulnerabilities within a Spark route handler to directly manipulate the `Response` object *provided by Spark*. They might inject malicious content into the response body, set malicious headers, or alter the response status code, all *through Spark's API*. This focuses on direct manipulation of the Spark `Response` object.

    • **Impact:**  Cross-site scripting (XSS) (enabling, not the core threat), HTTP response splitting, information disclosure, client-side attacks – all stemming from direct manipulation of Spark's response.

    • **Spark Component Affected:**  `Response` object and its methods (e.g., `body()`, `header()`, `status()`) *as provided by Spark*.

    • **Risk Severity:** High

    • **Mitigation Strategies:**
      • Carefully control how the Response object is modified within route handlers using Spark's API.
      • Avoid setting headers or body content based on unvalidated user input that is then passed to Spark's Response methods.
      • Use a templating engine (if applicable) with automatic output encoding to prevent XSS, ensuring it interacts correctly with Spark's response handling.
      • Implement Content Security Policy (CSP) to mitigate the impact of response manipulation, ensuring it's configured correctly within the Spark context.