mitigations.md

Mitigation Strategies Analysis for bradlarson/gpuimage

Mitigation Strategy: Validate User-Provided Filter Parameters

• Description:

  • Step 1: Identify all points in your application where user input can influence GPUImage filter selection or parameter values. This includes UI elements or API endpoints that configure GPUImage filters.
  • Step 2: Define strict validation rules for each input point, specifying data type, range, and format for filter parameters relevant to GPUImage filters.
  • Step 3: Implement input validation logic before passing user input to GPUImage functions. Use programming language features for validation and error handling.
  • Step 4: Reject invalid input and provide informative error messages. Do not process images with invalid GPUImage parameters.

• List of Threats Mitigated:

  • Filter Parameter Injection (Severity: Medium to High): Malicious users could inject harmful values into GPUImage filter parameters, causing crashes, unexpected behavior, or exploiting vulnerabilities in GPUImage or graphics drivers.
  • Denial of Service (DoS) via Resource Exhaustion (Severity: Medium): Extreme parameter values could lead to computationally expensive GPUImage filter operations, causing performance degradation or application freeze.

• Impact:

  • Filter Parameter Injection: High reduction in risk. Proper validation prevents injection attacks by ensuring only valid GPUImage parameters are processed.
  • Denial of Service (DoS) via Resource Exhaustion: Medium reduction in risk. Validation limits extreme values, reducing resource exhaustion through parameter manipulation in GPUImage filters.

• Currently Implemented: Partially - Input validation might exist in general application forms, but specific validation for GPUImage filter parameters is likely not implemented.

• Missing Implementation: Specific validation logic for GPUImage filter parameters is likely missing wherever user input configures GPUImage filters in the application's code.

Mitigation Strategy: Limit Allowed Filter Selection

• Description:

  • Step 1: Identify all GPUImage filters used in your application.
  • Step 2: Create a whitelist of GPUImage filters necessary for your application's functionality and reviewed for security.
  • Step 3: Restrict user filter selection to this whitelist. Prevent arbitrary filter specification or custom filter uploads to GPUImage.
  • Step 4: If filter selection is exposed, populate UI elements or API options only with filters from the GPUImage whitelist.

• List of Threats Mitigated:

  • Malicious Filter Exploitation (Severity: High): If GPUImage or specific filters have vulnerabilities, arbitrary filter selection increases the attack surface. Malicious users could trigger vulnerabilities by selecting specific GPUImage filters.
  • Shader Injection (Indirect) (Severity: Medium): Allowing a wide range of GPUImage filters increases the chance of encountering and exploiting a filter with a shader vulnerability within GPUImage.

• Impact:

  • Malicious Filter Exploitation: High reduction in risk. Whitelisting reduces the attack surface by limiting filters to a vetted set within GPUImage.
  • Shader Injection (Indirect): Medium reduction in risk. Reduces the chance of vulnerable shaders by limiting the GPUImage filter pool.

• Currently Implemented: Potentially Partially - The application might use a limited set of GPUImage filters functionally, but a security-driven whitelist might be absent.

• Missing Implementation: A formal, security-driven whitelist of allowed GPUImage filters is likely missing. Explicit checks to prevent using GPUImage filters outside the safe set are needed.

Mitigation Strategy: Static Shader Analysis and Review

• Description:

  • Step 1: Gather all shader code used by your application, including shaders provided by GPUImage and any custom shaders used with GPUImage.
  • Step 2: Perform manual code review of each shader, focusing on security vulnerabilities relevant to shader code in the context of GPUImage usage (buffer overflows, integer issues, etc.).
  • Step 3: Use static analysis tools for shader languages (GLSL, etc.) if available to automatically scan GPUImage shaders for vulnerabilities.
  • Step 4: Document findings and address vulnerabilities by modifying shader code or implementing mitigations in application logic interacting with GPUImage.

• List of Threats Mitigated:

  • Shader Vulnerabilities Exploitation (Severity: High): Vulnerabilities in GPUImage shader code can cause crashes, memory corruption, or potentially code execution if exploited through GPUImage.
  • Information Leakage via Shaders (Severity: Medium): GPUImage shaders might unintentionally leak information through output textures if not carefully designed.

• Impact:

  • Shader Vulnerabilities Exploitation: High reduction in risk. Shader analysis and remediation reduce the risk of exploiting vulnerabilities within GPUImage shaders.
  • Information Leakage via Shaders: Medium reduction in risk. Shader review helps prevent unintended information leakage from GPUImage shaders.

• Currently Implemented: Likely No - Static shader analysis and security reviews of GPUImage shaders are not common practice.

• Missing Implementation: Static shader analysis and security review of GPUImage shaders are likely completely missing. This should be implemented when using GPUImage.

Mitigation Strategy: Minimize Dynamic Shader Generation

• Description:

  • Step 1: Review application code to identify instances where shader code used with GPUImage is dynamically generated based on user input or external data.
  • Step 2: Refactor to eliminate or minimize dynamic shader generation for GPUImage.
  • Step 3: Prefer using pre-compiled and tested shaders statically included when working with GPUImage.
  • Step 4: If dynamic shader generation for GPUImage is unavoidable, implement extremely robust input sanitization and validation for all data used in shader construction.

• List of Threats Mitigated:

  • Shader Injection (Direct) (Severity: High): Dynamically generating shaders for GPUImage based on user input creates a direct pathway for shader injection attacks within the GPUImage pipeline.

• Impact:

  • Shader Injection (Direct): High reduction in risk. Eliminating dynamic shader generation removes the primary attack vector for direct shader injection in the context of GPUImage.

• Currently Implemented: Potentially Partially - The application might not intentionally generate shaders from user input for GPUImage, but indirect dynamic construction might occur through filter parameters.

• Missing Implementation: A conscious effort to eliminate dynamic shader generation for GPUImage and a codebase review to ensure no unintended dynamic shader construction is occurring is likely missing.

Mitigation Strategy: Control Image Processing Complexity

• Description:

  • Step 1: Implement limits on the maximum resolution of images processed by GPUImage.
  • Step 2: Limit the maximum number of filters applied in a GPUImage processing pipeline.
  • Step 3: Estimate computational cost of GPUImage filter combinations and reject requests exceeding a complexity threshold.
  • Step 4: Implement timeouts for GPUImage image processing operations to prevent resource monopolization.

• List of Threats Mitigated:

  • Denial of Service (DoS) via Resource Exhaustion (Severity: Medium to High): Processing large images or applying many complex GPUImage filters can exhaust GPU resources, leading to DoS.

• Impact:

  • Denial of Service (DoS) via Resource Exhaustion: Medium to High reduction in risk. Limiting image size and GPUImage filter complexity reduces potential for resource exhaustion DoS attacks.

• Currently Implemented: Potentially Partially - Implicit limits might exist due to UI or performance, but explicit, security-driven limits on GPUImage processing complexity are likely missing.

• Missing Implementation: Explicit controls and limits on image resolution, GPUImage filter count, and processing complexity, designed to prevent resource exhaustion DoS related to GPUImage, are likely missing.

Mitigation Strategy: Monitor GPU Resource Usage

• Description:

  • Step 1: Implement monitoring of GPU resource usage (memory, processing time, utilization) within your application, specifically during GPUImage operations.
  • Step 2: Set up alerts or logging to detect unusual spikes in GPU resource consumption related to GPUImage.
  • Step 3: If resource usage exceeds thresholds during GPUImage operations, implement mechanisms to terminate tasks, throttle requests, or alert administrators.

• List of Threats Mitigated:

  • Denial of Service (DoS) Detection and Mitigation (Severity: Medium): Monitoring helps detect DoS attacks that attempt to exhaust GPU resources via GPUImage, allowing for mitigation.
  • Detection of Anomalous Shader Behavior (Severity: Low to Medium): Unusual GPU resource usage patterns during GPUImage operations might indicate unexpected shader behavior or exploitation attempts.

• Impact:

  • Denial of Service (DoS) Detection and Mitigation: Medium reduction in risk. Monitoring provides visibility into resource usage during GPUImage operations and enables reactive mitigation.
  • Detection of Anomalous Shader Behavior: Low to Medium reduction in risk. Monitoring can provide early warning signs of issues related to GPUImage shaders.

• Currently Implemented: Likely No - GPU resource monitoring within application code, specifically for GPUImage operations, is not typical.

• Missing Implementation: Application-level GPU resource monitoring and automated response mechanisms specifically for GPUImage usage are likely missing.

Mitigation Strategy: Regularly Update GPUImage

• Description:

  • Step 1: Establish a process for regularly checking for updates to the gpuimage library.
  • Step 2: Subscribe to security advisories or release notes for gpuimage (if available).
  • Step 3: Test new gpuimage versions before production deployment.
  • Step 4: Apply gpuimage updates promptly, especially for security patches.

• List of Threats Mitigated:

  • Exploitation of Known GPUImage Vulnerabilities (Severity: High to Critical): Outdated GPUImage versions might contain known, exploitable vulnerabilities.

• Impact:

  • Exploitation of Known GPUImage Vulnerabilities: High reduction in risk. Regularly updating GPUImage patches known vulnerabilities, reducing exploitation risk.

• Currently Implemented: Potentially Partially - General dependency updates might occur, but security-focused updates for GPUImage might not be prioritized.

• Missing Implementation: A dedicated process for tracking GPUImage security updates and proactively applying them is likely missing.

Mitigation Strategy: Dependency Scanning

• Description:

  • Step 1: Integrate dependency scanning tools into your development pipeline to scan project dependencies, including gpuimage.
  • Step 2: Configure the scanner to identify known vulnerabilities in gpuimage and its dependencies.
  • Step 3: Review scanner reports and prioritize remediation of vulnerabilities in gpuimage or its dependencies. Update or implement workarounds.

• List of Threats Mitigated:

  • Exploitation of Known GPUImage and Dependency Vulnerabilities (Severity: High to Critical): GPUImage or its dependencies might have vulnerabilities. Dependency scanning helps identify these.

• Impact:

  • Exploitation of Known GPUImage and Dependency Vulnerabilities: High reduction in risk. Dependency scanning proactively identifies vulnerabilities in GPUImage and dependencies, allowing for timely remediation.

• Currently Implemented: Potentially Partially - Dependency management might exist, but security-focused dependency scanning tools might not be integrated for gpuimage specifically.

• Missing Implementation: Integration of security dependency scanning tools and a process for acting on vulnerability reports related to gpuimage are likely missing.