Physical AI Toolchain

Projects that follow the best practices below can voluntarily self-certify and show that they've achieved an Open Source Security Foundation (OpenSSF) best practices badge.

There is no set of practices that can guarantee that software will never have defects or vulnerabilities; even formal methods can fail if the specifications or assumptions are wrong. Nor is there any set of practices that can guarantee that a project will sustain a healthy and well-functioning development community. However, following best practices can help improve the results of projects. For example, some practices enable multi-person review before release, which can both help find otherwise hard-to-find technical vulnerabilities and help build trust and a desire for repeated interaction among developers from different companies. To earn a badge, all MUST and MUST NOT criteria must be met, all SHOULD criteria must be met OR be unmet with justification, and all SUGGESTED criteria must be met OR unmet (we want them considered at least). If you want to enter justification text as a generic comment, instead of being a rationale that the situation is acceptable, start the text block with '//' followed by a space. Feedback is welcome via the GitHub site as issues or pull requests There is also a mailing list for general discussion.

We gladly provide the information in several locales, however, if there is any conflict or inconsistency between the translations, the English version is the authoritative version.
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These are the Silver level criteria. You can also view the Passing or Gold level criteria.

Baseline Series: Baseline Level 1 Baseline Level 2 Baseline Level 3

        

 Basics 17/17 ●

  • General

    Note that other projects may use the same name.

    Physical AI Toolchain is an open-source, production-ready framework that integrates Microsoft Azure (https://azure.microsoft.com/) cloud services with NVIDIA's (https://developer.nvidia.com/) physical AI stack, accelerating robotics and physical AI developers to automate and scale data curation, augmentation, and evaluation across perception, mobility, imitation learning, and reinforcement learning pipelines.

    Please use SPDX license expression format; examples include "Apache-2.0", "BSD-2-Clause", "BSD-3-Clause", "GPL-2.0+", "LGPL-3.0+", "MIT", and "(BSD-2-Clause OR Ruby)". Do not include single quotes or double quotes.
    If there is more than one language, list them as comma-separated values (spaces optional) and sort them from most to least used. If there is a long list, please list at least the first three most common ones. If there is no language (e.g., this is a documentation-only or test-only project), use the single character "-". Please use a conventional capitalization for each language, e.g., "JavaScript".
    The Common Platform Enumeration (CPE) is a structured naming scheme for information technology systems, software, and packages. It is used in a number of systems and databases when reporting vulnerabilities.

  • Prerequisites


    Enough for a badge!

    The project MUST achieve a passing level badge. [achieve_passing]

  • Basic project website content


    Enough for a badge!

    The information on how to contribute MUST include the requirements for acceptable contributions (e.g., a reference to any required coding standard). (URL required) [contribution_requirements]

    CONTRIBUTING.md clearly documents all contribution requirements: Conventional Commits message format (feat:, fix:, docs:, chore:, etc.), branch naming using category prefixes, testing policy requiring tests for new features and regression tests for bug fixes (≥50% of bug fix PRs must include regression tests), coding standards enforced by Ruff (Python), markdownlint (Markdown), PSScriptAnalyzer (PowerShell), yaml-lint (YAML), and cspell (spelling). The PR template and 16-job pr-validation.yml CI pipeline automatically enforce these requirements on every PR.

    Evidence:


  • Project oversight


    Enough for a badge!

    The project SHOULD have a legal mechanism where all developers of non-trivial amounts of project software assert that they are legally authorized to make these contributions. The most common and easily-implemented approach for doing this is by using a Developer Certificate of Origin (DCO), where users add "signed-off-by" in their commits and the project links to the DCO website. However, this MAY be implemented as a Contributor License Agreement (CLA), or other legal mechanism. (URL required) [dco]
    The DCO is the recommended mechanism because it's easy to implement, tracked in the source code, and git directly supports a "signed-off" feature using "commit -s". To be most effective it is best if the project documentation explains what "signed-off" means for that project. A CLA is a legal agreement that defines the terms under which intellectual works have been licensed to an organization or project. A contributor assignment agreement (CAA) is a legal agreement that transfers rights in an intellectual work to another party; projects are not required to have CAAs, since having CAA increases the risk that potential contributors will not contribute, especially if the receiver is a for-profit organization. The Apache Software Foundation CLAs (the individual contributor license and the corporate CLA) are examples of CLAs, for projects which determine that the risks of these kinds of CLAs to the project are less than their benefits.

    Justification: Microsoft Contributor License Agreement (CLA) is used in lieu of DCO. Every external pull request is gated by the Microsoft CLA bot at https://cla.opensource.microsoft.com, which records the contributor's explicit grant of rights.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/CONTRIBUTING.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/GOVERNANCE.md, https://cla.opensource.microsoft.com


    Enough for a badge!

    The project MUST clearly define and document its project governance model (the way it makes decisions, including key roles). (URL required) [governance]
    There needs to be some well-established documented way to make decisions and resolve disputes. In small projects, this may be as simple as "the project owner and lead makes all final decisions". There are various governance models, including benevolent dictator and formal meritocracy; for more details, see Governance models. Both centralized (e.g., single-maintainer) and decentralized (e.g., group maintainers) approaches have been successfully used in projects. The governance information does not need to document the possibility of creating a project fork, since that is always possible for FLOSS projects.

    Justification: Governance and decision-making are documented in GOVERNANCE.md, including the corporate-sponsored maintainer model, role matrix, escalation model, and contribution acceptance process.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/GOVERNANCE.md


    Enough for a badge!

    The project MUST adopt a code of conduct and post it in a standard location. (URL required) [code_of_conduct]
    Projects may be able to improve the civility of their community and to set expectations about acceptable conduct by adopting a code of conduct. This can help avoid problems before they occur and make the project a more welcoming place to encourage contributions. This should focus only on behavior within the community/workplace of the project. Example codes of conduct are the Linux kernel code of conduct, the Contributor Covenant Code of Conduct, the Debian Code of Conduct, the Ubuntu Code of Conduct, the Fedora Code of Conduct, the GNOME Code Of Conduct, the KDE Community Code of Conduct, the Python Community Code of Conduct, The Ruby Community Conduct Guideline, and The Rust Code of Conduct.

    Justification: The project adopts the Microsoft Open Source Code of Conduct and posts it in the standard .github/CODE_OF_CONDUCT.md location.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/.github/CODE_OF_CONDUCT.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/CONTRIBUTING.md


    Enough for a badge!

    The project MUST clearly define and publicly document the key roles in the project and their responsibilities, including any tasks those roles must perform. It MUST be clear who has which role(s), though this might not be documented in the same way. (URL required) [roles_responsibilities]
    The documentation for governance and roles and responsibilities may be in one place.

    Justification: Key roles and responsibilities are documented in governance, with team-based ownership in CODEOWNERS.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/GOVERNANCE.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/.github/CODEOWNERS


    Enough for a badge!

    The project MUST be able to continue with minimal interruption if any one person dies, is incapacitated, or is otherwise unable or unwilling to continue support of the project. In particular, the project MUST be able to create and close issues, accept proposed changes, and release versions of software, within a week of confirmation of the loss of support from any one individual. This MAY be done by ensuring someone else has any necessary keys, passwords, and legal rights to continue the project. Individuals who run a FLOSS project MAY do this by providing keys in a lockbox and a will providing any needed legal rights (e.g., for DNS names). (URL required) [access_continuity]

    Justification: Project access continuity is documented through multi-admin/team ownership and succession practices in governance, with team-based code ownership in CODEOWNERS.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/GOVERNANCE.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/.github/CODEOWNERS


    Enough for a badge!

    The project SHOULD have a "bus factor" of 2 or more. (URL required) [bus_factor]
    A "bus factor" (aka "truck factor") is the minimum number of project members that have to suddenly disappear from a project ("hit by a bus") before the project stalls due to lack of knowledgeable or competent personnel. The truck-factor tool can estimate this for projects on GitHub. For more information, see Assessing the Bus Factor of Git Repositories by Cosentino et al.

    Justification: Bus factor is at least 2 through team-based maintainership, multi-admin governance, and @microsoft/edge-ai-core-dev code ownership.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/GOVERNANCE.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/.github/CODEOWNERS


  • Documentation


    Enough for a badge!

    The project MUST have a documented roadmap that describes what the project intends to do and not do for at least the next year. (URL required) [documentation_roadmap]
    The project might not achieve the roadmap, and that's fine; the purpose of the roadmap is to help potential users and contributors understand the intended direction of the project. It need not be detailed.

    Justification: The roadmap documents at least one year of project intent, milestones, and success metrics.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/contributing/ROADMAP.md


    Enough for a badge!

    The project MUST include documentation of the architecture (aka high-level design) of the software produced by the project. If the project does not produce software, select "not applicable" (N/A). (URL required) [documentation_architecture]
    A software architecture explains a program's fundamental structures, i.e., the program's major components, the relationships among them, and the key properties of these components and relationships.

    Justification: High-level architecture is documented in docs/contributing/architecture.md and domain READMEs for infrastructure, training, evaluation, data management, fleet deployment, and fleet intelligence.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/contributing/architecture.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/infrastructure/README.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/training/README.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/evaluation/README.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/data-management/README.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/fleet-deployment/README.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/fleet-intelligence/README.md


    Enough for a badge!

    The project MUST document what the user can and cannot expect in terms of security from the software produced by the project (its "security requirements"). (URL required) [documentation_security]
    These are the security requirements that the software is intended to meet.

    Justification: Security expectations are documented in the security policy, threat model, and release verification guide.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/SECURITY.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/security/threat-model.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/security/release-verification.md


    Enough for a badge!

    The project MUST provide a "quick start" guide for new users to help them quickly do something with the software. (URL required) [documentation_quick_start]
    The idea is to show users how to get started and make the software do anything at all. This is critically important for potential users to get started.

    Justification: Quick-start and getting-started guidance is documented in the repository README and getting-started documentation.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/README.md, https://github.com/microsoft/physical-ai-toolchain/tree/main/docs/getting-started


    Enough for a badge!

    The project MUST make an effort to keep the documentation consistent with the current version of the project results (including software produced by the project). Any known documentation defects making it inconsistent MUST be fixed. If the documentation is generally current, but erroneously includes some older information that is no longer true, just treat that as a defect, then track and fix as usual. [documentation_current]
    The documentation MAY include information about differences or changes between versions of the software and/or link to older versions of the documentation. The intent of this criterion is that an effort is made to keep the documentation consistent, not that the documentation must be perfect.

    Justification: Documentation maintenance expectations and component update practices are documented, and the changelog records documentation/process updates.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/contributing/documentation-maintenance.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/contributing/component-updates.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/CHANGELOG.md


    Enough for a badge!

    The project repository front page and/or website MUST identify and hyperlink to any achievements, including this best practices badge, within 48 hours of public recognition that the achievement has been attained. (URL required) [documentation_achievements]
    An achievement is any set of external criteria that the project has specifically worked to meet, including some badges. This information does not need to be on the project website front page. A project using GitHub can put achievements on the repository front page by adding them to the README file.

    Justification: The project currently the OpenSSF passing badge link to the README within 48 hours after the Silver badge is awarded. The badge will be updated automatically once award is issued.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/README.md


  • Accessibility and internationalization


    Enough for a badge!

    The project (both project sites and project results) SHOULD follow accessibility best practices so that persons with disabilities can still participate in the project and use the project results where it is reasonable to do so. [accessibility_best_practices]
    For web applications, see the Web Content Accessibility Guidelines (WCAG 2.0) and its supporting document Understanding WCAG 2.0; see also W3C accessibility information. For GUI applications, consider using the environment-specific accessibility guidelines (such as Gnome, KDE, XFCE, Android, iOS, Mac, and Windows). Some TUI applications (e.g. `ncurses` programs) can do certain things to make themselves more accessible (such as `alpine`'s `force-arrow-cursor` setting). Most command-line applications are fairly accessible as-is. This criterion is often N/A, e.g., for program libraries. Here are some examples of actions to take or issues to consider:
    • Provide text alternatives for any non-text content so that it can be changed into other forms people need, such as large print, braille, speech, symbols or simpler language ( WCAG 2.0 guideline 1.1)
    • Color is not used as the only visual means of conveying information, indicating an action, prompting a response, or distinguishing a visual element. ( WCAG 2.0 guideline 1.4.1)
    • The visual presentation of text and images of text has a contrast ratio of at least 4.5:1, except for large text, incidental text, and logotypes ( WCAG 2.0 guideline 1.4.3)
    • Make all functionality available from a keyboard (WCAG guideline 2.1)
    • A GUI or web-based project SHOULD test with at least one screen-reader on the target platform(s) (e.g. NVDA, Jaws, or WindowEyes on Windows; VoiceOver on Mac & iOS; Orca on Linux/BSD; TalkBack on Android). TUI programs MAY work to reduce overdraw to prevent redundant reading by screen-readers.

    Justification: Accessibility practices are documented for project sites and user-facing documentation.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/contributing/accessibility.md


    Enough for a badge!

    The software produced by the project SHOULD be internationalized to enable easy localization for the target audience's culture, region, or language. If internationalization (i18n) does not apply (e.g., the software doesn't generate text intended for end-users and doesn't sort human-readable text), select "not applicable" (N/A). [internationalization]
    Localization "refers to the adaptation of a product, application or document content to meet the language, cultural and other requirements of a specific target market (a locale)." Internationalization is the "design and development of a product, application or document content that enables easy localization for target audiences that vary in culture, region, or language." (See W3C's "Localization vs. Internationalization".) Software meets this criterion simply by being internationalized. No localization for another specific language is required, since once software has been internationalized it's possible for others to work on localization.

    Justification: N/A. The project primarily produces infrastructure-as-code, training scripts, telemetry tooling, and developer/operator tooling. It does not ship an end-user product with localized UI text requirements. The dataviewer is a developer tool rather than a consumer-facing localized application.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/data-management/README.md


  • Other


    Enough for a badge!

    If the project sites (website, repository, and download URLs) store passwords for authentication of external users, the passwords MUST be stored as iterated hashes with a per-user salt by using a key stretching (iterated) algorithm (e.g., Argon2id, Bcrypt, Scrypt, or PBKDF2). If the project sites do not store passwords for this purpose, select "not applicable" (N/A). [sites_password_security]
    Note that the use of GitHub meets this criterion. This criterion only applies to passwords used for authentication of external users into the project sites (aka inbound authentication). If the project sites must log in to other sites (aka outbound authentication), they may need to store authorization tokens for that purpose differently (since storing a hash would be useless). This applies criterion crypto_password_storage to the project sites, similar to sites_https.

    Justification: N/A. The project sites and tooling do not store user passwords. Authentication is delegated to GitHub, Microsoft Entra ID/MSAL, GitHub OIDC, and Azure identity services.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/security/threat-model.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/docs/security/workflow-permissions.md


 Change Control 1/1 ●

 Reporting 3/3 ●

  • Bug-reporting process


    Enough for a badge!

    The project MUST use an issue tracker for tracking individual issues. [report_tracker]

    GitHub Issues serves as the project's issue tracker. It is publicly readable, searchable, and filterable. Issues support labels, milestones, and assignees for triage and tracking. The 7 issue templates apply automatic labels for categorization. Closed issues remain in the searchable archive. The tracker is integrated with PRs via GitHub's "Fixes #NNN" linking, providing traceability from bug report to fix.

    Evidence:


  • Vulnerability report process


    Enough for a badge!

    The project MUST give credit to the reporter(s) of all vulnerability reports resolved in the last 12 months, except for the reporter(s) who request anonymity. If there have been no vulnerabilities resolved in the last 12 months, select "not applicable" (N/A). (URL required) [vulnerability_report_credit]

    Justification: MSFT has a dedicated vulnerability reporting process as noted in links form teh security.md file

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/SECURITY.md


    Enough for a badge!

    The project MUST have a documented process for responding to vulnerability reports. (URL required) [vulnerability_response_process]
    This is strongly related to vulnerability_report_process, which requires that there be a documented way to report vulnerabilities. It also related to vulnerability_report_response, which requires response to vulnerability reports within a certain time frame.

    Justification: Vulnerability reporting and response timelines are documented through Microsoft Security Response Center guidance and support policy.

    Evidence URLs: https://github.com/microsoft/physical-ai-toolchain/blob/main/SECURITY.md, https://github.com/microsoft/physical-ai-toolchain/blob/main/SUPPORT.md


 Quality 19/19 ●

 Security 13/13 ●

 Analysis 2/2 ●

  • Static code analysis


    Enough for a badge!

    The project MUST use at least one static analysis tool with rules or approaches to look for common vulnerabilities in the analyzed language or environment, if there is at least one FLOSS tool that can implement this criterion in the selected language. [static_analysis_common_vulnerabilities]
    Static analysis tools that are specifically designed to look for common vulnerabilities are more likely to find them. That said, using any static tools will typically help find some problems, so we are suggesting but not requiring this for the 'passing' level badge.

    CodeQL runs with both "security-extended" and "security-and-quality" query suites — these are GitHub's most comprehensive security query sets, specifically designed to detect common vulnerabilities including: SQL injection, cross-site scripting (XSS), path traversal, code injection, insecure deserialization, SSRF, hardcoded credentials, and other OWASP Top 10 categories. The security-extended suite goes beyond the default security queries to catch additional vulnerability patterns. Results are uploaded as SARIF to the GitHub Security tab, providing a centralized view of all detected security findings.

    Evidence:


  • Dynamic code analysis


    Enough for a badge!

    If the software produced by the project includes software written using a memory-unsafe language (e.g., C or C++), then at least one dynamic tool (e.g., a fuzzer or web application scanner) MUST be routinely used in combination with a mechanism to detect memory safety problems such as buffer overwrites. If the project does not produce software written in a memory-unsafe language, choose "not applicable" (N/A). [dynamic_analysis_unsafe]
    Examples of mechanisms to detect memory safety problems include Address Sanitizer (ASAN) (available in GCC and LLVM), Memory Sanitizer, and valgrind. Other potentially-used tools include thread sanitizer and undefined behavior sanitizer. Widespread assertions would also work.

    This criterion applies to projects with C/C++ code that should enable memory-safety checks (AddressSanitizer, MemorySanitizer, UndefinedBehaviorSanitizer, etc.). The project contains no C or C++ code — the codebase is entirely Python, TypeScript, Terraform HCL, PowerShell, and shell scripts. Python and TypeScript are memory-safe languages. Therefore, memory-safety analyzers for compiled languages are not applicable.

    Evidence:



This data is available under the Community Data License Agreement – Permissive, Version 2.0 (CDLA-Permissive-2.0). This means that a Data Recipient may share the Data, with or without modifications, so long as the Data Recipient makes available the text of this agreement with the shared Data. Please credit Bill Berry and the OpenSSF Best Practices badge contributors.

Project badge entry owned by: Bill Berry.
Entry created on 2026-03-16 22:18:49 UTC, last updated on 2026-05-16 22:30:42 UTC. Last achieved passing badge on 2026-03-17 00:03:32 UTC.