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BUSINESS POSTURE

The candle project is a minimalist machine learning framework written in Rust, with a focus on performance and ease of use. It aims to provide a leaner alternative to existing Python-centric frameworks, particularly for inference workloads.

  • Business priorities and goals:

    • Provide a high-performance, low-resource consumption machine learning inference framework.
    • Offer an easy-to-use API for developers familiar with Rust and machine learning concepts.
    • Enable efficient deployment of machine learning models in resource-constrained environments (edge devices, embedded systems).
    • Foster a community around Rust-based machine learning.
    • Potentially reduce the operational costs associated with machine learning inference by improving efficiency.

  • Most important business risks:

    • Slower adoption compared to established Python frameworks due to the Rust ecosystem being less mature in machine learning.
    • Performance bottlenecks or vulnerabilities discovered after wider adoption, impacting user trust.
    • Lack of community support and contributions, hindering project growth and maintenance.
    • Security vulnerabilities in dependencies or the core framework code, leading to potential exploits in applications using candle.
    • Compatibility issues with various hardware and software environments, limiting deployment options.

SECURITY POSTURE

  • Security controls:

    • security control: Code is written in Rust, which inherently provides memory safety and reduces the risk of certain classes of vulnerabilities like buffer overflows. Implemented in: Project codebase (Rust language features).
    • security control: Use of unsafe Rust blocks is minimized. Implemented in: Project codebase (code review and Rust language features).
    • security control: Standard software development practices are likely followed, including version control (Git), issue tracking (GitHub Issues), and pull requests. Implemented in: GitHub repository and development workflow.

  • Accepted risks:

    • accepted risk: Limited formal security audits or penetration testing, especially in the early stages of the project.
    • accepted risk: Potential vulnerabilities in third-party dependencies used by the framework.
    • accepted risk: Security vulnerabilities might be discovered as the project matures and is used in more diverse environments.
    • accepted risk: Reliance on community contributions for bug fixes and security patches, which might have variable response times.

  • Recommended security controls:

    • recommended security control: Implement automated Static Application Security Testing (SAST) tools in the CI/CD pipeline to identify potential vulnerabilities in the codebase.
    • recommended security control: Implement automated Dependency Scanning to identify known vulnerabilities in third-party dependencies.
    • recommended security control: Conduct regular security code reviews, especially for critical components and contributions from external developers.
    • recommended security control: Establish a vulnerability disclosure policy and process to handle security reports from the community.
    • recommended security control: Consider fuzz testing to identify unexpected behavior and potential vulnerabilities in input processing.

  • Security requirements:

    • Authentication: Not directly applicable to a machine learning framework library. Authentication is the responsibility of the applications that use candle.
    • Authorization: Not directly applicable to a machine learning framework library. Authorization is the responsibility of the applications that use candle.
    • Input validation: The framework should perform robust input validation on any external data it processes, such as model weights, input tensors, and configuration parameters, to prevent unexpected behavior or vulnerabilities. Implemented in: Framework code, specifically in data loading and processing modules.
    • Cryptography: If candle implements or uses cryptographic operations (e.g., for secure model loading or communication), ensure that well-vetted and secure cryptographic libraries are used correctly. Implemented in: Framework code, specifically in modules related to cryptography if applicable.

DESIGN

C4 CONTEXT

flowchart LR
    subgraph "Organization Context"
      style "Organization Context" fill:#f9f,stroke:#333,stroke-width:2px
      User[/"Machine Learning\nEngineers &\nResearchers"/]
      Application[/"Applications\nUsing Candle"/]
      Data[/"Training Data\n& Models"/]
    end
    Candle[/"Candle\nML Framework"/]
    PythonEcosystem[/"Python ML\nEcosystem\n(PyTorch, TF)"/]

    User --> Candle
    Application --> Candle
    Data --> Candle
    Candle --> PythonEcosystem

    style Candle fill:#ccf,stroke:#333,stroke-width:2px
Loading
  • Context Diagram Elements:
      • Name: Machine Learning Engineers & Researchers
      • Type: Person
      • Description: Users who develop, train, and deploy machine learning models and applications. They use candle to build and run inference workloads.
      • Responsibilities: Utilize candle to implement machine learning models, evaluate performance, and integrate it into applications.
      • Security controls: Secure development practices, access control to development environments.
      • Name: Applications Using Candle
      • Type: Software System
      • Description: Various applications (web services, mobile apps, embedded systems, etc.) that integrate candle to perform machine learning inference.
      • Responsibilities: Utilize candle library to execute machine learning models, handle input data, and process output predictions.
      • Security controls: Application-level security controls (authentication, authorization, input validation, output encoding, etc.).
      • Name: Training Data & Models
      • Type: External System / Data Store
      • Description: Datasets used to train machine learning models and the resulting trained models that are loaded and used by candle for inference. Models can be stored in various formats and locations.
      • Responsibilities: Provide data for model training and storage of trained models. Model serving infrastructure is responsible for secure access and delivery of models.
      • Security controls: Access control to data storage, encryption of data at rest and in transit, model integrity checks.
      • Name: Candle ML Framework
      • Type: Software System
      • Description: The candle machine learning framework itself, providing APIs and functionalities for building and running machine learning inference.
      • Responsibilities: Provide efficient and secure machine learning inference capabilities, manage model loading and execution, handle input and output data.
      • Security controls: Secure coding practices, input validation, dependency management, vulnerability scanning, build process security.
      • Name: Python ML Ecosystem (PyTorch, TF)
      • Type: External System
      • Description: Existing machine learning frameworks and tools in the Python ecosystem that candle aims to be an alternative to, and potentially interoperate with (e.g., model conversion).
      • Responsibilities: Provide established machine learning functionalities and serve as a benchmark for comparison.
      • Security controls: Not directly managed by candle project, but security posture of these ecosystems can influence the overall security landscape.

C4 CONTAINER

flowchart LR
    subgraph "Candle ML Framework"
      style "Candle ML Framework" fill:#ccf,stroke:#333,stroke-width:2px
      CoreLib[/"Core Library\n(Rust Crates)"/]
      Examples[/"Examples\n& Tutorials"/]
      Bindings[/"Language Bindings\n(Optional)"/]
      Documentation[/"Documentation\nWebsite"/]
    end

    User[/"Machine Learning\nEngineers &\nResearchers"/] --> CoreLib
    Application[/"Applications\nUsing Candle"/] --> CoreLib
    Examples --> CoreLib
    Bindings --> CoreLib

    style CoreLib fill:#ddf,stroke:#333,stroke-width:2px
    style Examples fill:#ddf,stroke:#333,stroke-width:2px
    style Bindings fill:#ddf,stroke:#333,stroke-width:2px
    style Documentation fill:#ddf,stroke:#333,stroke-width:2px
Loading
  • Container Diagram Elements:
      • Name: Core Library (Rust Crates)
      • Type: Container - Library
      • Description: The core candle framework implemented as Rust crates. This includes modules for tensor operations, neural network layers, model loading, and inference execution.
      • Responsibilities: Provide the fundamental functionalities of the machine learning framework, including numerical computation, model execution, and API for users.
      • Security controls: Secure coding practices in Rust, input validation within the library, dependency scanning for Rust crates, fuzz testing of core functionalities.
      • Name: Examples & Tutorials
      • Type: Container - Application
      • Description: Example applications and tutorials demonstrating how to use the candle library. These serve as learning resources and showcases of the framework's capabilities.
      • Responsibilities: Provide practical examples of using candle, demonstrate best practices, and help users get started with the framework.
      • Security controls: Examples should follow secure coding practices to avoid demonstrating insecure patterns. Input validation in examples should be present where user input is processed.
      • Name: Language Bindings (Optional)
      • Type: Container - Library/API
      • Description: Optional language bindings (e.g., for Python or other languages) that allow using candle from other programming environments.
      • Responsibilities: Enable interoperability with other languages and ecosystems, broaden the usability of candle.
      • Security controls: Secure development of bindings, ensuring proper data handling and API security when crossing language boundaries. Input validation at the binding interface.
      • Name: Documentation Website
      • Type: Container - Web Application
      • Description: Website hosting the documentation for candle, including API references, tutorials, and guides.
      • Responsibilities: Provide comprehensive documentation for users, facilitate learning and adoption of the framework.
      • Security controls: Standard web application security controls (HTTPS, input validation for any user-generated content, protection against common web vulnerabilities).

DEPLOYMENT

Deployment of candle itself is as a library. Applications using candle will be deployed in various ways. Let's consider a common scenario: deploying a web service that uses candle for inference in a cloud environment.

flowchart LR
    subgraph "Cloud Environment (AWS, GCP, Azure)"
      style "Cloud Environment (AWS, GCP, Azure)" fill:#eef,stroke:#333,stroke-width:2px
      LoadBalancer[/"Load Balancer"/]
      WebServer[/"Web Server\nInstances"/]
      ModelStorage[/"Model Storage\n(Object Storage)"/]
    end

    Client[/"Web Client"/] --> LoadBalancer
    LoadBalancer --> WebServer
    WebServer --> CandleLib[["Candle Library\n(within Web App)"]]
    WebServer --> ModelStorage

    subgraph "WebServer Instance"
      style "WebServer Instance" fill:#fdf,stroke:#333,stroke-width:2px
      CandleLib
    end

    style LoadBalancer fill:#ddf,stroke:#333,stroke-width:2px
    style WebServer fill:#ddf,stroke:#333,stroke-width:2px
    style ModelStorage fill:#ddf,stroke:#333,stroke-width:2px
    style CandleLib fill:#cdf,stroke:#333,stroke-width:2px
    style Client fill:#ddf,stroke:#333,stroke-width:2px
Loading
  • Deployment Diagram Elements:
      • Name: Web Client
      • Type: External System
      • Description: Users accessing the web service through web browsers or other HTTP clients.
      • Responsibilities: Send requests to the web service and receive predictions.
      • Security controls: Client-side security controls (browser security, user authentication if applicable).
      • Name: Load Balancer
      • Type: Infrastructure Component
      • Description: Distributes incoming traffic across multiple web server instances for scalability and availability.
      • Responsibilities: Traffic distribution, health checks, SSL termination.
      • Security controls: DDoS protection, SSL/TLS configuration, access control lists.
      • Name: Web Server Instances
      • Type: Infrastructure Component (Compute Instance)
      • Description: Virtual machines or containers running the web application that uses candle for inference.
      • Responsibilities: Host the web application, handle HTTP requests, load models using candle, perform inference, and return predictions.
      • Security controls: Instance hardening, OS security patching, network firewalls, intrusion detection systems, application-level firewalls, secure configuration management.
      • Name: Candle Library (within Web App)
      • Type: Software Component (Library)
      • Description: The candle library integrated into the web application code.
      • Responsibilities: Load machine learning models from storage, perform inference on input data, and provide predictions to the web application.
      • Security controls: Input validation within the application code using candle, secure model loading practices, memory safety provided by Rust.
      • Name: Model Storage (Object Storage)
      • Type: Infrastructure Component (Data Storage)
      • Description: Cloud object storage service (e.g., AWS S3, GCP Cloud Storage, Azure Blob Storage) used to store trained machine learning models.
      • Responsibilities: Securely store model files, provide access to web server instances for model loading.
      • Security controls: Access control policies (IAM), encryption at rest, encryption in transit (HTTPS), versioning, audit logging.

BUILD

flowchart LR
    Developer[/"Developer\nWorkstation"/] --> VCS[/"Version Control\n(GitHub)"/]
    VCS --> CI[/"CI System\n(GitHub Actions)"/]
    CI --> BuildEnv[/"Build Environment\n(Secure Runner)"/]
    BuildEnv --> ArtifactRepo[/"Artifact Repository\n(GitHub Releases,\nContainer Registry)"/]

    subgraph "CI System (GitHub Actions)"
      style "CI System (GitHub Actions)" fill:#eef,stroke:#333,stroke-width:2px
      SAST[/"SAST Scanner"/]
      DependencyCheck[/"Dependency\nCheck"/]
      Test[/"Unit & Integration\nTests"/]
      Build[/"Build & Package"/]
      Sign[/"Code Signing\n(Optional)"/]
      Publish[/"Publish Artifacts"/]

      CI --> SAST
      CI --> DependencyCheck
      CI --> Test
      CI --> Build
      CI --> Sign
      CI --> Publish

      BuildEnv --> SAST
      BuildEnv --> DependencyCheck
      BuildEnv --> Test
      BuildEnv --> Build
      BuildEnv --> Sign
      ArtifactRepo --> Publish
    end

    style Developer fill:#ddf,stroke:#333,stroke-width:2px
    style VCS fill:#ddf,stroke:#333,stroke-width:2px
    style CI fill:#ddf,stroke:#333,stroke-width:2px
    style BuildEnv fill:#ddf,stroke:#333,stroke-width:2px
    style ArtifactRepo fill:#ddf,stroke:#333,stroke-width:2px
    style SAST fill:#cdf,stroke:#333,stroke-width:2px
    style DependencyCheck fill:#cdf,stroke:#333,stroke-width:2px
    style Test fill:#cdf,stroke:#333,stroke-width:2px
    style Build fill:#cdf,stroke:#333,stroke-width:2px
    style Sign fill:#cdf,stroke:#333,stroke-width:2px
    style Publish fill:#cdf,stroke:#333,stroke-width:2px
Loading
  • Build Process Elements:
      • Name: Developer Workstation
      • Type: Development Environment
      • Description: Developer's local machine where code is written, tested locally, and committed to version control.
      • Responsibilities: Code development, local testing, committing code changes.
      • Security controls: Developer workstation security (OS hardening, antivirus, endpoint protection), secure coding practices.
      • Name: Version Control (GitHub)
      • Type: Code Repository
      • Description: GitHub repository hosting the candle project source code.
      • Responsibilities: Source code management, version history, collaboration, pull request reviews.
      • Security controls: Access control to repository, branch protection, code review process, audit logging.
      • Name: CI System (GitHub Actions)
      • Type: CI/CD Platform
      • Description: GitHub Actions used for automated build, test, and release processes.
      • Responsibilities: Automate build pipeline, run security checks, execute tests, build artifacts, and publish releases.
      • Security controls: Secure CI/CD pipeline configuration, access control to CI/CD workflows, secret management, audit logging.
      • Name: Build Environment (Secure Runner)
      • Type: Build Infrastructure
      • Description: Secure environment where build jobs are executed. This could be GitHub-hosted runners or self-hosted runners with enhanced security.
      • Responsibilities: Provide a secure and isolated environment for building and testing the code.
      • Security controls: Runner isolation, hardened runner environment, access control, secure credentials management, monitoring.
      • Name: SAST Scanner
      • Type: Security Tool
      • Description: Static Application Security Testing tool integrated into the CI pipeline to automatically scan the codebase for potential vulnerabilities.
      • Responsibilities: Identify potential security flaws in the code before deployment.
      • Security controls: Regularly updated vulnerability rules, configuration to match project needs, reporting of findings.
      • Name: Dependency Check
      • Type: Security Tool
      • Description: Tool to scan project dependencies for known vulnerabilities.
      • Responsibilities: Identify vulnerable dependencies and alert developers to update or mitigate.
      • Security controls: Regularly updated vulnerability database, automated scanning in CI, reporting of vulnerable dependencies.
      • Name: Unit & Integration Tests
      • Type: Testing Framework
      • Description: Automated unit and integration tests to ensure code quality and functionality.
      • Responsibilities: Verify code correctness, prevent regressions, improve code reliability.
      • Security controls: Tests should cover security-relevant functionalities, test data should be handled securely.
      • Name: Build & Package
      • Type: Build Toolchain (Rust Cargo)
      • Description: Rust's build system (Cargo) used to compile the code, create libraries, and packages.
      • Responsibilities: Compile source code, manage dependencies, create build artifacts (crates, binaries).
      • Security controls: Dependency management (Cargo.lock), build reproducibility, secure build configuration.
      • Name: Code Signing (Optional)
      • Type: Security Tool
      • Description: Digitally signing build artifacts to ensure integrity and authenticity.
      • Responsibilities: Provide assurance that artifacts are from a trusted source and haven't been tampered with.
      • Security controls: Secure key management, code signing infrastructure, verification process.
      • Name: Artifact Repository (GitHub Releases, Container Registry)
      • Type: Artifact Storage
      • Description: Storage for build artifacts, such as Rust crates published to crates.io, GitHub Releases, or container images.
      • Responsibilities: Securely store and distribute build artifacts.
      • Security controls: Access control to artifact repository, integrity checks, versioning, audit logging.

RISK ASSESSMENT

  • Critical business processes we are trying to protect:

    • Successful adoption and use of the candle framework by the machine learning community and application developers.
    • Maintaining the performance and reliability of the framework for inference workloads.
    • Protecting the integrity and availability of the framework's codebase and build artifacts.
    • Ensuring the security of applications built using candle.

  • Data we are trying to protect and their sensitivity:

    • Source code of the candle framework (intellectual property, integrity). Sensitivity: High.
    • Build artifacts (binaries, libraries) (integrity, availability). Sensitivity: Medium.
    • Machine learning models used with candle (confidentiality, integrity, availability - depending on the model and data it's trained on). Sensitivity: Variable, potentially High if models are proprietary or trained on sensitive data.
    • User data processed by applications using candle (confidentiality, integrity, availability - highly sensitive depending on the application). Sensitivity: Variable, potentially High.
    • Credentials and secrets used in the build and deployment processes (confidentiality, integrity). Sensitivity: High.

QUESTIONS & ASSUMPTIONS

  • Questions:

    • What is the intended scope of security for the candle project? Is it primarily focused on the framework itself, or also on providing guidance for secure application development using candle?
    • Are there any specific compliance requirements or industry standards that the candle project needs to adhere to?
    • What is the process for handling security vulnerabilities reported by the community? Is there a dedicated security team or point of contact?
    • Are there plans for formal security audits or penetration testing of the candle framework in the future?
    • What is the expected level of security expertise among the target users of candle? Should the framework provide built-in security features or rely on users to implement security measures in their applications?

  • Assumptions:

    • The candle project is developed with a general awareness of secure coding practices, but formal security processes might be limited in the early stages.
    • The primary focus of the project is currently on functionality and performance, with security being a secondary but important consideration.
    • The project relies on the inherent security benefits of Rust, but additional security measures are needed to address a broader range of threats.
    • Users of candle are expected to implement application-level security controls to protect their applications and data.
    • The project is open to community contributions and feedback, including security-related suggestions and reports.