sec-design.md

Okay, let's create a design document for the Gluon-CV project.

BUSINESS POSTURE

Gluon-CV is an open-source project aiming to provide a comprehensive and easy-to-use toolkit for computer vision research and development. It democratizes access to state-of-the-art pre-trained models and training scripts, fostering innovation and accelerating development in the field.

Priorities:

• Ease of use and accessibility for researchers and developers.
• Reproducibility of research results.
• Providing high-quality, state-of-the-art models.
• Community engagement and contribution.
• Maintainability and extensibility of the codebase.

Goals:

• Become a leading toolkit for computer vision research.
• Enable faster prototyping and development of computer vision applications.
• Facilitate the sharing and dissemination of research findings.
• Lower the barrier to entry for newcomers to the field.

Business Risks:

• Model misuse: Pre-trained models could be used for malicious purposes (e.g., deepfakes, surveillance).
• Bias in models: Models trained on biased datasets could perpetuate and amplify societal biases.
• Lack of adoption: The project might fail to gain traction within the community.
• Maintenance burden: Maintaining a large and complex codebase can be challenging.
• Competition: Other computer vision toolkits might offer superior features or performance.
• Security vulnerabilities in dependencies: Vulnerabilities in third-party libraries could expose users to risks.

SECURITY POSTURE

Existing Security Controls:

• security control: Code reviews: The project uses GitHub's pull request system, which inherently includes code review before merging changes. (Described in contribution guidelines).
• security control: Issue tracking: Security vulnerabilities can be reported and tracked using GitHub's issue tracker. (Described in contribution guidelines).
• security control: Basic input validation: The library likely performs some basic input validation to prevent crashes or unexpected behavior, although this is not explicitly documented as a security feature. (Implied in the code).
• security control: Use of a linter: The project uses a linter (flake8) to enforce code style and identify potential errors. (Described in contribution guidelines).

Accepted Risks:

• accepted risk: Model misuse: The project acknowledges the potential for misuse of pre-trained models but relies on users to act responsibly.
• accepted risk: Reliance on external dependencies: The project depends on several external libraries (e.g., MXNet, NumPy, OpenCV), and vulnerabilities in these dependencies could affect Gluon-CV.

Recommended Security Controls:

• security control: Implement a Software Bill of Materials (SBOM) to track all dependencies and their versions.
• security control: Integrate Static Application Security Testing (SAST) tools into the build process to automatically identify potential vulnerabilities.
• security control: Perform regular security audits of the codebase and dependencies.
• security control: Develop a clear security policy and vulnerability disclosure process.
• security control: Consider implementing runtime checks to detect and mitigate potential attacks, such as adversarial examples.
• security control: Provide guidance to users on how to securely deploy and use Gluon-CV models.

Security Requirements:

• Authentication: Not directly applicable, as Gluon-CV is a library, not a service. Authentication would be handled by the application using the library.
• Authorization: Not directly applicable, as Gluon-CV is a library. Authorization would be handled by the application using the library.
• Input Validation:
  • The library should validate the shape and type of input tensors to prevent crashes and unexpected behavior.
  • The library should handle potentially malicious input gracefully, without crashing or exposing sensitive information.
• Cryptography:
  • If the library handles sensitive data (e.g., API keys, user credentials), it should use appropriate cryptographic techniques to protect this data. This is not apparent in the current scope.
• Data handling:
  • If loading models from external sources (URLs), verify the integrity and authenticity of the downloaded data, for example, by checking hashes.

DESIGN

C4 CONTEXT

graph LR
    User["User (Researcher/Developer)"] -- Uses --> GluonCV["Gluon-CV Toolkit"]
    GluonCV -- Uses --> MXNet["MXNet/Other Deep Learning Framework"]
    GluonCV -- Downloads --> PretrainedModels["Pre-trained Models (Model Zoo)"]
    GluonCV -- Reads --> Datasets["Datasets (e.g., ImageNet, COCO)"]
    MXNet -- Uses --> Hardware["Hardware (CPU/GPU/TPU)"]
    User -- Provides --> Datasets
    User -- Downloads --> PretrainedModels

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Element Description:

• Element:

  • Name: User (Researcher/Developer)
  • Type: Person
  • Description: A researcher or developer who uses Gluon-CV to build and train computer vision models.
  • Responsibilities: Provides data, configures models, trains models, evaluates results, deploys models.
  • Security controls: Responsible for securing their own environment and data.

• Element:

  • Name: Gluon-CV Toolkit
  • Type: Software System
  • Description: The Gluon-CV library itself, providing APIs for model building, training, and evaluation.
  • Responsibilities: Provides pre-trained models, training scripts, data loaders, and utility functions.
  • Security controls: Input validation, dependency management, code reviews.

• Element:

  • Name: MXNet/Other Deep Learning Framework
  • Type: Software System
  • Description: The underlying deep learning framework used by Gluon-CV (primarily MXNet, but potentially others).
  • Responsibilities: Provides low-level operations for tensor manipulation and automatic differentiation.
  • Security controls: Relies on the security of the chosen framework.

• Element:

  • Name: Pre-trained Models (Model Zoo)
  • Type: Data
  • Description: A collection of pre-trained models that can be downloaded and used with Gluon-CV.
  • Responsibilities: Provides starting points for training or fine-tuning models.
  • Security controls: Model integrity checks (e.g., hash verification) are recommended.

• Element:

  • Name: Datasets (e.g., ImageNet, COCO)
  • Type: Data
  • Description: Standard computer vision datasets used for training and evaluating models.
  • Responsibilities: Provides labeled data for training and evaluation.
  • Security controls: Data integrity and provenance should be considered.

• Element:

  • Name: Hardware (CPU/GPU/TPU)
  • Type: Infrastructure
  • Description: The hardware on which the models are trained and executed.
  • Responsibilities: Provides computational resources.
  • Security controls: Hardware-level security features (e.g., secure boot, trusted execution environments) may be relevant.

C4 CONTAINER

graph LR
    User["User (Researcher/Developer)"] -- Uses --> GluonCVAPI["Gluon-CV API"]
    GluonCVAPI -- Uses --> ModelZoo["Model Zoo"]
    GluonCVAPI -- Uses --> DataLoaders["Data Loaders"]
    GluonCVAPI -- Uses --> TrainingScripts["Training Scripts"]
    GluonCVAPI -- Uses --> Utils["Utilities"]
    ModelZoo -- Provides --> PretrainedModels["Pre-trained Models"]
    DataLoaders -- Reads --> Datasets["Datasets"]
    TrainingScripts -- Uses --> ModelZoo
    TrainingScripts -- Uses --> DataLoaders
    TrainingScripts -- Uses --> MXNet["MXNet/Other Framework"]
    Utils -- Provides --> HelperFunctions["Helper Functions"]
    MXNet -- Uses --> Hardware["Hardware (CPU/GPU)"]
    User -- Provides --> Datasets

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Element Description:

• Element:

  • Name: Gluon-CV API
  • Type: API
  • Description: The main interface for interacting with the Gluon-CV library.
  • Responsibilities: Provides a high-level, user-friendly API for common computer vision tasks.
  • Security controls: Input validation.

• Element:

  • Name: Model Zoo
  • Type: Container
  • Description: A collection of pre-trained models.
  • Responsibilities: Provides access to pre-trained models for various tasks.
  • Security controls: Model integrity checks (recommended).

• Element:

  • Name: Data Loaders
  • Type: Container
  • Description: Components for loading and pre-processing data.
  • Responsibilities: Efficiently loads and pre-processes data for training and evaluation.
  • Security controls: Input validation, handling of corrupted data.

• Element:

  • Name: Training Scripts
  • Type: Container
  • Description: Scripts for training and evaluating models.
  • Responsibilities: Provides examples and templates for training models.
  • Security controls: Secure coding practices.

• Element:

  • Name: Utilities
  • Type: Container
  • Description: Helper functions and utilities.
  • Responsibilities: Provides common functionality used throughout the library.
  • Security controls: Secure coding practices.

• Element:

  • Name: Pre-trained Models
  • Type: Data
  • Description: The actual pre-trained model weights and architectures.
  • Responsibilities: Provides the learned parameters for the models.
  • Security controls: Integrity checks (recommended).

• Element:

  • Name: Datasets
  • Type: Data
  • Description: The data used for training and evaluation.
  • Responsibilities: Provides the input data for the models.
  • Security controls: Data integrity and provenance.

• Element:

  • Name: MXNet/Other Framework
  • Type: Software System
  • Description: The underlying deep learning framework.
  • Responsibilities: Performs the low-level computations.
  • Security controls: Relies on the security of the framework.

• Element:

  • Name: Hardware (CPU/GPU)
  • Type: Infrastructure
  • Description: The hardware used for computation.
  • Responsibilities: Executes the computations.
  • Security controls: Hardware-level security.

• Element:

  • Name: Helper Functions
  • Type: Code
  • Description: Utility functions used by other components.
  • Responsibilities: Provides reusable code.
  • Security controls: Secure coding practices.

• Element:

  • Name: User (Researcher/Developer)
  • Type: Person
  • Description: A researcher or developer using the library.
  • Responsibilities: Uses the library to build and train models.
  • Security controls: Responsible for their own environment.

DEPLOYMENT

Gluon-CV, being a library, can be deployed in various ways depending on the user's application. Here are a few possibilities, with one described in detail:

Possible Deployment Solutions:

1. Local Machine: For research and development, users often run Gluon-CV directly on their local machines (CPU or GPU).
2. Cloud VMs: Users can deploy Gluon-CV on cloud virtual machines (e.g., AWS EC2, Google Compute Engine, Azure VMs).
3. Containerized Environments: Gluon-CV can be packaged into Docker containers and deployed on container orchestration platforms like Kubernetes.
4. Serverless Functions: Parts of the inference pipeline could be deployed as serverless functions (e.g., AWS Lambda, Google Cloud Functions).

Chosen Deployment Solution (Containerized Environment - Kubernetes):

graph LR
    Developer["Developer"] -- Pushes --> DockerRegistry["Docker Registry (e.g., Docker Hub, ECR)"]
    DockerRegistry -- PulledBy --> KubernetesCluster["Kubernetes Cluster"]
    KubernetesCluster -- Runs --> GluonCVPod["Gluon-CV Pod(s)"]
    GluonCVPod -- Contains --> GluonCVContainer["Gluon-CV Container"]
    GluonCVContainer -- Uses --> ModelStorage["Model Storage (e.g., S3, GCS)"]
    GluonCVContainer -- Uses --> DataStorage["Data Storage (e.g., S3, GCS)"]
    User["User"] -- InteractsWith --> KubernetesCluster

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Element Description:

• Element:

  • Name: Developer
  • Type: Person
  • Description: The person building and deploying the Gluon-CV application.
  • Responsibilities: Creates the Docker image, configures the Kubernetes deployment.
  • Security controls: Secure coding practices, access control to the Docker registry and Kubernetes cluster.

• Element:

  • Name: Docker Registry (e.g., Docker Hub, ECR)
  • Type: System
  • Description: A registry for storing Docker images.
  • Responsibilities: Stores the Gluon-CV Docker image.
  • Security controls: Access control, image scanning.

• Element:

  • Name: Kubernetes Cluster
  • Type: System
  • Description: A cluster of machines managed by Kubernetes.
  • Responsibilities: Orchestrates the deployment and scaling of the Gluon-CV application.
  • Security controls: Network policies, role-based access control (RBAC), pod security policies.

• Element:

  • Name: Gluon-CV Pod(s)
  • Type: Container Group
  • Description: One or more instances of the Gluon-CV application running in the Kubernetes cluster.
  • Responsibilities: Runs the Gluon-CV code.
  • Security controls: Resource limits, security context.

• Element:

  • Name: Gluon-CV Container
  • Type: Container
  • Description: The Docker container containing the Gluon-CV library and its dependencies.
  • Responsibilities: Provides the runtime environment for the Gluon-CV application.
  • Security controls: Minimal base image, regular updates.

• Element:

  • Name: Model Storage (e.g., S3, GCS)
  • Type: System
  • Description: Storage for pre-trained models.
  • Responsibilities: Provides persistent storage for models.
  • Security controls: Access control, encryption at rest and in transit.

• Element:

  • Name: Data Storage (e.g., S3, GCS)
  • Type: System
  • Description: Storage for datasets.
  • Responsibilities: Provides persistent storage for data.
  • Security controls: Access control, encryption at rest and in transit.

• Element:

  • Name: User
  • Type: Person
  • Description: The person interacting with the deployed application.
  • Responsibilities: Sends requests to the application.
  • Security controls: Authentication and authorization (handled by the application).

BUILD

graph LR
    Developer["Developer"] -- CommitsCode --> GitRepository["Git Repository (GitHub)"]
    GitRepository -- Triggers --> CIEnvironment["CI Environment (GitHub Actions)"]
    CIEnvironment -- Runs --> BuildSteps["Build Steps"]
    BuildSteps -- Performs --> Linting["Linting (flake8)"]
    BuildSteps -- Performs --> UnitTests["Unit Tests"]
    BuildSteps -- Performs --> BuildPackage["Build Package"]
    BuildSteps -- Performs --> SAST["SAST (Optional)"]
    BuildPackage -- Outputs --> PythonPackage["Python Package (e.g., Wheel)"]
    PythonPackage -- UploadedTo --> PackageRepository["Package Repository (e.g., PyPI)"]

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Build Process Description:

1. Developer commits code to the Git repository (GitHub).
2. The commit triggers the CI environment (GitHub Actions, based on the .github/workflows directory).
3. The CI environment runs a series of build steps:
   • Linting: Code style checks using flake8.
   • Unit Tests: Runs unit tests to verify the correctness of the code.
   • Build Package: Builds the Python package (e.g., a wheel file).
   • SAST (Optional/Recommended): Integrates Static Application Security Testing tools to scan for vulnerabilities.
4. The built package is then uploaded to a package repository (e.g., PyPI) for distribution.

Security Controls in Build Process:

• security control: Code Reviews: Enforced through the pull request system on GitHub.
• security control: Linting: Identifies potential code quality and style issues.
• security control: Unit Tests: Helps ensure the correctness of the code and prevent regressions.
• security control: SAST (Recommended): Automatically scans for vulnerabilities in the code.
• security control: Dependency Management: Using requirements.txt or similar to manage dependencies. Regular updates are crucial.
• security control: CI/CD Pipeline Security: Securing the CI/CD environment itself (e.g., access control, secrets management).

RISK ASSESSMENT

Critical Business Processes:

• Model Training: The ability to train new computer vision models efficiently and accurately.
• Model Deployment: The ability to deploy trained models for inference in various environments.
• Research Reproducibility: Ensuring that research results can be easily reproduced by others.
• Community Engagement: Maintaining an active and engaged community of users and contributors.

Data Sensitivity:

• Pre-trained Models: The model weights themselves are not typically considered sensitive data, but their misuse can have significant consequences.
• Datasets: Datasets used for training may contain sensitive information, depending on their content (e.g., images of people, medical data). Gluon-CV itself doesn't store these datasets, but users must be aware of the sensitivity of the data they use.
• User Data: If Gluon-CV is integrated into an application that collects user data, that data would be considered sensitive. This is outside the scope of Gluon-CV as a library.

QUESTIONS & ASSUMPTIONS

Questions:

• Are there any specific compliance requirements (e.g., GDPR, HIPAA) that need to be considered? (Assumption: No specific compliance requirements beyond general best practices.)
• What is the expected scale of deployment? (Assumption: Deployments will range from small-scale research projects to larger-scale applications.)
• What are the specific threat models that the project is most concerned about? (Assumption: The primary concerns are model misuse, bias, and vulnerabilities in dependencies.)
• What level of security expertise is expected from users of the library? (Assumption: Users have a basic understanding of security concepts, but may not be security experts.)
• Are there plans to integrate with specific cloud providers or services? (Assumption: The library should be cloud-agnostic, but may provide examples for specific cloud platforms.)

Assumptions:

• BUSINESS POSTURE: The primary goal is to provide a research and development toolkit, not a production-ready service.
• SECURITY POSTURE: The project relies on community contributions and best practices for security, rather than formal security certifications.
• DESIGN: The library is designed to be flexible and extensible, allowing users to customize it for their specific needs.
• The main deep learning framework used is MXNet, but other frameworks may be supported.
• Users are responsible for securing their own deployment environments.
• The project will follow semantic versioning.
• The project will use GitHub for issue tracking and code management.