mitigations.md

Mitigation Strategies Analysis for tencent/ncnn

Mitigation Strategy: Validate Input Dimensions and Format

• Description:

  1. Identify ncnn Model Input Requirements: Consult the documentation or model definition files (.param files) for the specific ncnn model being used. Determine the exact expected input dimensions (e.g., width, height, channels for images, tensor shapes) and data type (e.g., float32, int8) as required by the ncnn model's input layers.
  2. Implement ncnn Input Validation Function: Create a function that receives the input data immediately before it is passed to the ncnn::Net::input() function or equivalent ncnn API calls. This function is specifically designed to validate data for ncnn.
  3. Dimension Checks (ncnn Tensor Shapes): Within the validation function, check if the input data's dimensions (shape) precisely match the expected tensor shapes defined in the ncnn model's .param file. For example, if an input layer expects a tensor of shape [1, 3, 224, 224], verify the input data conforms to this shape.
  4. Data Type Checks (ncnn Data Types): Verify that the data type of the input is compatible with the ncnn model's input layer data type requirements (e.g., float32, int8, uint8). Convert the input data type if necessary and safe before passing it to ncnn, or reject the input if incompatible.
  5. Error Handling (ncnn Input Errors): If validation fails (input dimensions or data type are incorrect for ncnn), implement error handling that prevents the invalid input from being processed by ncnn. Log the validation failure with details about the expected vs. actual input for debugging ncnn integration issues.

• List of Threats Mitigated:

  • Crashes within ncnn due to Unexpected Input Shapes/Types (High Severity): Providing input with incorrect dimensions or data types specifically for ncnn can lead to crashes within the ncnn library itself due to out-of-bounds memory access, type mismatches, or unexpected operations during ncnn's tensor processing.
  • Incorrect ncnn Model Output (Medium Severity): Even if not crashing, incorrect input dimensions or types for ncnn can lead to nonsensical or unreliable model outputs from ncnn, affecting the application's functionality that relies on ncnn's results.

• Impact:

  • Crashes within ncnn due to Unexpected Input Shapes/Types: Significantly reduces risk of ncnn-specific crashes.
  • Incorrect ncnn Model Output: Significantly reduces risk of incorrect results from ncnn due to input mismatches.

• Currently Implemented:

  • Partially Implemented in projects using ncnn: Developers often handle input shapes during data preprocessing before ncnn, but explicit validation immediately before ncnn input and specifically for ncnn's requirements is less common.

• Missing Implementation:

  • Explicit Validation Functions for ncnn Input: Dedicated functions specifically designed to validate input dimensions and data types right before feeding data into ncnn.
  • Automated Validation for ncnn Input: Integration of validation checks into unit tests or integration tests that specifically test the ncnn model integration with various input scenarios.

Mitigation Strategy: Model Provenance and Verification

• Description:

  1. Establish Trusted Sources for ncnn Models: Identify and document trusted sources specifically for the ncnn models used in the application. This should be sources known to provide legitimate and unmodified ncnn model files (.param and .bin).
  2. Prefer Official or Model Creator Sources for ncnn Models: Prioritize downloading ncnn models from official repositories of the model creators or reputable sources known for providing ncnn-compatible models.
  3. Implement Checksum Verification for ncnn Model Files: If model sources provide checksums (e.g., SHA256) for the ncnn model files (.param and .bin), download and verify the checksums specifically for these ncnn model files after downloading.
  4. Digital Signature Verification for ncnn Models (if available): If model sources provide digital signatures for ncnn models, implement signature verification to ensure the integrity and authenticity of the downloaded ncnn model files.
  5. Document ncnn Model Provenance: Keep records of where each ncnn model (.param and .bin files) was downloaded from and when, for auditing and tracking the origin of the ncnn models used in the application.

• List of Threats Mitigated:

  • Supply Chain Attacks - Malicious ncnn Model Replacement (High Severity): An attacker could replace legitimate ncnn model files (.param, .bin) with malicious ones, leading to compromised application behavior specifically through the ncnn model's actions, data breaches if the model is designed to leak data, or other security incidents triggered by the malicious model.
  • ncnn Model Corruption (Medium Severity): ncnn model files (.param, .bin) can be corrupted during download or storage, leading to unpredictable ncnn behavior, crashes within ncnn, or incorrect inference results from ncnn.

• Impact:

  • Supply Chain Attacks - Malicious ncnn Model Replacement: Significantly reduces risk of using compromised ncnn models.
  • ncnn Model Corruption: Moderately reduces risk of using corrupted ncnn models.

• Currently Implemented:

  • Rarely Implemented for ncnn Models: Model provenance and verification are often overlooked specifically for ncnn models. Developers might download ncnn models from convenient locations without rigorous verification of the .param and .bin files.

• Missing Implementation:

  • Checksum/Signature Verification for ncnn Model Files: Automated processes to verify the integrity of ncnn model files (.param, .bin) using checksums or digital signatures.
  • ncnn Model Provenance Tracking: Systematic tracking of the sources and versions of ncnn models (.param, .bin files) used in the application.
  • Secure Storage for ncnn Models: Storing ncnn model files in secure locations with access control to prevent unauthorized modification or replacement of these files.

Mitigation Strategy: Regularly Update ncnn Library

• Description:

  1. Monitor ncnn Releases and Security Advisories: Regularly check the official ncnn GitHub repository for new releases, bug fixes, and security advisories specifically related to ncnn.
  2. Subscribe to ncnn Security Notifications (if available): If the ncnn project offers security mailing lists or notification channels, subscribe to them to receive timely updates about ncnn security issues.
  3. Establish ncnn Library Update Process: Define a process for regularly updating the ncnn library in your application's build system or dependency management. This should include testing the updated ncnn library to ensure compatibility with your application and ncnn models.
  4. Prioritize ncnn Security Updates: Treat security updates for the ncnn library with high priority and apply them promptly to patch known vulnerabilities in ncnn.
  5. Dependency Updates for ncnn: When updating ncnn, also review and update its dependencies to ensure all components related to ncnn's build and runtime environment are up-to-date.

• List of Threats Mitigated:

  • Exploitation of Known ncnn Library Vulnerabilities (High Severity): Outdated versions of the ncnn library may contain known security vulnerabilities within the ncnn code itself that attackers can exploit. Regular updates patch these ncnn-specific vulnerabilities.

• Impact:

  • Exploitation of Known ncnn Library Vulnerabilities: Significantly reduces risk of vulnerabilities within the ncnn library.

• Currently Implemented:

  • Variable Implementation for ncnn: Some projects might have a general dependency update process, but it's not always consistently applied or prioritized specifically for ncnn security updates.

• Missing Implementation:

  • Automated ncnn Dependency Scanning: Using tools to automatically scan for outdated dependencies, specifically the ncnn library, and identify known vulnerabilities in ncnn.
  • Regular ncnn Update Schedule: Establishing a regular schedule for checking and applying updates to the ncnn library.
  • Testing Updated ncnn Integration: Integrating testing into the ncnn update process to verify that the updated ncnn library works correctly with the application's ncnn model integration and inference workflows.

Mitigation Strategy: Resource Limits for ncnn Processes

• Description:

  1. Identify ncnn Inference Processes: Determine how ncnn inference is executed in your application (e.g., as a separate process, within a thread). Pinpoint the specific processes or threads that are actively running ncnn's inference computations.
  2. Implement Resource Limiting Mechanisms for ncnn Processes: Use operating system or containerization features to set resource limits specifically for the processes running ncnn inference. This can include:
     • CPU Limits for ncnn: Limit the CPU cores or CPU time available to ncnn inference processes.
     • Memory Limits for ncnn: Limit the maximum memory that ncnn inference processes can consume.
     • Execution Time Limits (Timeouts) for ncnn Inference: Set timeouts for ncnn inference operations. If inference takes longer than expected, terminate the ncnn operation to prevent indefinite resource consumption.
  3. Configure ncnn Resource Limits Appropriately: Set resource limits based on the expected resource consumption of the ncnn model being used and the overall system capacity. Avoid setting limits too low, which could negatively impact ncnn inference performance.
  4. Monitor ncnn Resource Usage: Monitor the resource usage of ncnn inference processes to ensure that the configured limits are effective in preventing excessive resource consumption by ncnn and are not causing performance bottlenecks for ncnn inference.

• List of Threats Mitigated:

  • Denial of Service (DoS) via ncnn Resource Exhaustion (High Severity): Uncontrolled resource consumption specifically by ncnn processes (due to malicious inputs processed by ncnn or issues within the ncnn model itself) can lead to DoS. Resource limits for ncnn processes mitigate this.

• Impact:

  • Denial of Service (DoS) via ncnn Resource Exhaustion: Significantly reduces risk of DoS caused by ncnn-related resource issues.

• Currently Implemented:

  • Rarely Implemented Directly for ncnn Processes: Resource limits are more commonly applied at the container or system level, but not often specifically configured for individual processes or threads dedicated to ncnn inference within an application.

• Missing Implementation:

  • Process-Specific Resource Limits for ncnn Inference: Implementing resource limits that are specifically targeted at the processes or threads actively running ncnn inference.
  • Dynamic ncnn Resource Adjustment: Potentially adjust resource limits for ncnn processes based on system load, input characteristics, or the specific ncnn model being used.

Mitigation Strategy: Isolate ncnn Processes (Sandboxing)

• Description:

  1. Run ncnn Inference in Isolated Processes: Execute ncnn inference operations in separate processes, rather than directly within the main application process. This creates a clear separation between the main application logic and the ncnn library execution environment.
  2. Apply Sandboxing Techniques to ncnn Processes: Use operating system features or sandboxing technologies (e.g., containers, seccomp, AppArmor) to restrict the capabilities and permissions of processes specifically running ncnn inference.
  3. Minimize Permissions for ncnn Processes: Grant ncnn processes only the minimum necessary permissions required to perform inference. This includes read-only access to ncnn model files, limited network access (if needed by ncnn, which is usually not the case for core inference), and restricted access to other system resources.
  4. Secure Inter-Process Communication (IPC) with ncnn Processes: If ncnn processes need to communicate with the main application (e.g., to receive input data or return inference results), use secure IPC mechanisms and carefully validate all data exchanged between the main application and the sandboxed ncnn processes.

• List of Threats Mitigated:

  • Privilege Escalation from ncnn Vulnerabilities (High Severity): If a vulnerability in the ncnn library itself allows for code execution, sandboxing ncnn processes can prevent an attacker from escalating privileges or gaining access to sensitive system resources beyond the isolated ncnn environment.
  • Lateral Movement after ncnn Compromise (Medium Severity): Sandboxing ncnn processes limits an attacker's ability to move laterally within the system if the ncnn process is compromised. The attacker's access is confined to the sandbox.
  • Data Breaches due to ncnn Exploitation (Medium Severity): Sandboxing ncnn processes can restrict an attacker's access to sensitive data if the ncnn process is exploited. The attacker's ability to access data outside the sandbox is limited.

• Impact:

  • Privilege Escalation from ncnn Vulnerabilities: Significantly reduces risk of privilege escalation originating from ncnn vulnerabilities.
  • Lateral Movement after ncnn Compromise: Moderately reduces risk of lateral movement following a compromise of ncnn processes.
  • Data Breaches due to ncnn Exploitation: Moderately reduces risk of data breaches resulting from exploitation of ncnn processes.

• Currently Implemented:

  • Rarely Implemented Specifically for ncnn Processes: Sandboxing is more common in containerized deployments in general, but not often specifically configured to isolate ncnn inference processes as a distinct security measure within an application.

• Missing Implementation:

  • Dedicated Sandboxing for ncnn Inference Processes: Implementing sandboxing specifically to isolate the execution of ncnn inference operations.
  • Fine-grained Permission Control for ncnn Sandboxes: Applying very restrictive permissions to ncnn processes within the sandbox, limiting their access to system resources and data to the absolute minimum required for ncnn inference.