mitigations.md

Mitigation Strategies Analysis for mxgmn/wavefunctioncollapse

Mitigation Strategy: Request Timeout for Wavefunction Collapse Generation Process

• Description:

  1. When initiating a wavefunction collapse generation using the library, start a timer concurrently.
  2. Determine a reasonable maximum duration for the wavefunctioncollapse algorithm to execute based on expected complexity and performance. This duration should be configured based on testing and acceptable latency for your application.
  3. If the timer expires before the wavefunctioncollapse algorithm completes, interrupt or terminate the execution of the wavefunctioncollapse process. This prevents the algorithm from running indefinitely.
  4. Handle the timeout event gracefully in your application code, ensuring resources are released and an appropriate error message is returned to the user or logged for monitoring.

  • Threats Mitigated:

    • Denial of Service (DoS) due to computationally intensive wavefunctioncollapse executions (High Severity). Uncontrolled or excessively complex inputs can cause the algorithm to run for an unacceptably long time, consuming server resources and potentially leading to a DoS.

  • Impact:

    • High risk reduction for DoS threats related to long-running wavefunctioncollapse processes. Effectively limits the maximum resource consumption per generation request.

  • Currently Implemented:

    • Yes, a timeout mechanism is implemented in the backend service that utilizes the wavefunctioncollapse library. The timeout is set to 60 seconds and is applied directly to the execution of the wavefunctioncollapse function call.

  • Missing Implementation:

    • No missing implementation. Timeout is consistently applied to all calls to the wavefunctioncollapse library within the backend service.

Mitigation Strategy: Input Model Image Size Limits for Wavefunction Collapse

• Description:

  1. Before passing a model image to the wavefunctioncollapse library, implement a check on the image file size.
  2. Establish a maximum allowed file size for model images that are used as input for the wavefunctioncollapse algorithm. This limit should be determined based on performance testing and resource considerations for your server.
  3. If an input model image exceeds the defined size limit, reject the image and prevent it from being processed by the wavefunctioncollapse library. Return an error to the user indicating the size limit.
  4. Configure this size limit to be easily adjustable in the application's settings.

  • Threats Mitigated:

    • Denial of Service (DoS) by providing excessively large model images to wavefunctioncollapse (Medium Severity). Larger images generally increase the computational complexity and memory usage of the wavefunctioncollapse algorithm.
    • Potential for memory exhaustion or out-of-memory errors within the wavefunctioncollapse library due to very large input images (Medium Severity).

  • Impact:

    • Medium risk reduction for DoS and memory exhaustion threats related to large input images. Limits the resource demand placed on the wavefunctioncollapse algorithm by input image size.

  • Currently Implemented:

    • Yes, input model image size validation is implemented in the API endpoint that receives model images for wavefunctioncollapse processing. The maximum allowed size is set to 5MB.

  • Missing Implementation:

    • No missing implementation. Size validation is enforced before the model image is used as input to the wavefunctioncollapse library.

Mitigation Strategy: Parameter Whitelisting and Validation for Wavefunction Collapse Configuration

• Description:

  1. Identify all configurable parameters that your application exposes to users which directly influence the behavior of the wavefunctioncollapse algorithm (e.g., tile size, symmetry settings, allowed patterns if exposed).
  2. Create a strict whitelist of allowed parameter names and their permissible values or ranges. This whitelist should be based on the expected and safe usage of the wavefunctioncollapse library within your application's context.
  3. Before passing any user-provided parameters to the wavefunctioncollapse library, validate them against this whitelist.
  4. Reject any requests that include parameters not in the whitelist or parameters with values outside of the allowed ranges. Return an error message indicating the invalid parameters.
  5. Sanitize validated parameter values before passing them to the wavefunctioncollapse library to ensure they are in the expected format and prevent any unexpected behavior.

  • Threats Mitigated:

    • Unexpected behavior or errors in wavefunctioncollapse due to invalid or malicious parameter configurations (Low to Medium Severity). Incorrect parameters could lead to algorithm instability, errors, or unexpected outputs.
    • Potential for future vulnerabilities if parameter handling is not robust and allows for unintended manipulation of the wavefunctioncollapse algorithm's execution (Low Severity).

  • Impact:

    • Low to Medium risk reduction for unexpected behavior and potential future vulnerabilities. Ensures that the wavefunctioncollapse library is used with expected and validated configurations.

  • Currently Implemented:

    • Partially implemented. Validation exists for some key parameters like tile size, ensuring they are within reasonable numerical ranges. However, a comprehensive whitelist and validation for all configurable parameters related to wavefunctioncollapse is not fully in place.

  • Missing Implementation:

    • Missing complete parameter whitelisting and validation for all configurable options of the wavefunctioncollapse library that are exposed through the application's API or configuration. Need to expand validation to cover all relevant parameters and their allowed values according to the intended usage of wavefunctioncollapse.