Report On: Fetch issues

Analysis of Open Issues

Notable Open Issue

• Issue #64: Python 3.11.x compatible pip install
  • This issue is critical as it pertains to compatibility with the latest version of Python (3.11.x). The inability to install pyrit using pip for Python 3.11.x could be a significant blocker for users who have upgraded to the latest Python version.
  • The issue was created 2 days ago and edited 1 day ago, indicating active discussion and concern.
  • Comments from romanlutz and dlmgary suggest that the team is aware of the issue and plans to add support for newer versions of Python in the near future. However, no ETA has been provided, which adds uncertainty for users needing immediate support for Python 3.11.x.
  • The suggested workaround is to create a conda environment with Python 3.10, but this may not be ideal for all users, especially those who prefer or require the use of Python 3.11.x.
  • The mention of Pyenv by wearetyomsmnv and the team's preference for conda over Poetry provides insight into the team's approach to environment management and dependency installation.

Uncertainties & TODOs

• A specific timeline for when support for Python 3.11.x will be added is not provided, which could impact planning for users and developers who depend on pyrit.
• The decision-making process behind choosing conda as the preferred method over other tools like Poetry or Pyenv is not detailed, which might be relevant for contributors or users who are accustomed to different workflows.

Analysis of Closed Issues

Recent Closed Issue

• Issue #9: Action required: migrate or opt-out of migration to GitHub inside Microsoft
  • This issue was closed 40 days ago after being created 44 days ago.
  • It involved a policy decision regarding the migration of repositories within Microsoft's GitHub organization.
  • The repository opted out of migrating to GitHub inside Microsoft, as indicated by romanlutz. This decision suggests that the repository may be related to open source projects or requires collaboration with third parties, which necessitates remaining on public GitHub.
  • The bot response indicates that this opt-out decision will need to be reviewed every 120 days.

General Context from Closed Issues

• The recent closure of Issue #9 without significant discussion suggests that the team was confident in their decision to opt-out of internal migration, which aligns with open-source collaboration practices.
• There are no other recently discussed closed issues provided in this context, so we cannot derive further trends or context from closed issues.

Summary

The primary concern in the current open issues is Issue #64, which affects users trying to install pyrit on Python 3.11.x. This compatibility problem needs addressing promptly as it can hinder adoption by new users or frustrate current users looking to upgrade their Python environments.

The closed issue Issue #9 provides some context into the project's operational decisions and compliance with organizational policies but does not directly impact the technical aspects of the project.

Moving forward, it would be beneficial for the project maintainers to provide an estimated timeline for resolving Issue #64 and to consider offering more detailed justifications for their environmental and tooling preferences to better align with community practices and expectations.

Report On: Fetch pull requests

Analysis of Pull Requests

Open Pull Requests

PR #65: Add support to OpenAI API to use official or custom endpoints

• Summary: Adds the ability to communicate with both official OpenAI API and custom LLMs by changing the base URL.
• Status: Recently created, active discussion regarding Contributor License Agreement (CLA) and request for additional tests.
• Notable: The contributor has been responsive and is adding requested tests. This PR seems important as it extends the flexibility of the chat engine.

PR #57: Add release instructions

• Summary: Provides instructions for creating a new release.
• Status: Created 4 days ago, edited 3 days ago. No recent activity.
• Notable: The PR includes documentation changes but no code changes. It's important for project maintainability and should be reviewed for completeness and clarity.

Closed Pull Requests

PR #66: fix: correct typo in variable name

• Summary: A simple typo fix in a variable name.
• Status: Created and merged within a day.
• Notable: Quick fix, good response time, no issues.

PR #63: Include datasets in package

• Summary: Addresses an issue where the datasets folder wasn't installed correctly, potentially leading to conflicts.
• Status: Created, edited, and merged within two days.
• Notable: Important for ensuring that dataset conflicts are avoided. Good response and resolution time.

PR #62: adding data directories

• Summary: Similar to PR #63, aimed at including datasets into the package.
• Status: Not merged, closed after 2 days.
• Notable: This PR was closed without merging, likely because PR #63 addressed the same issue more effectively.

PR #61: Add datasets folder to wheel

• Summary: Ensures that the datasets folder is included in the wheel for distribution.
• Status: Created and merged within 3 days.
• Notable: Essential for package integrity when users don't provide a dataset and use the default one.

PR #60: Fixing bug with pypi package directory

• Summary: Fixes an issue with the .env structure when installing with pip.
• Status: Created and merged within 3 days.
• Notable: Important for user experience during installation.

PR #59: Add Details to Contributor Guide About Forking/PRs

• Summary: Adds information about contributing via Git to the Contributor Guide.
• Status: Created and merged within 3 days.
• Notable: Enhances contributor documentation, making it easier for new contributors to get started.

PR #58: Fix hyperlinks and updated package version

• Summary: Fixes hyperlinks in documentation and updates package version numbers to match pyproject.toml.
• Status: Created, edited, and merged within 4 days.
• Notable: Important for maintaining accurate documentation. Discussion about versioning indicates a need for a strategy to link notebook versions to corresponding package versions.

PR #56: Cleanup notebook outputs

• Summary: Cleans up Jupyter notebook outputs in preparation for demos/deployment guides.
• Status: Created and merged within 4 days.
• Notable: Good housekeeping practice before sharing notebooks publicly.

PR #55: Add learn link to README

• Summary: Adds a link to the Microsoft Learn page in the README file.
• Status: Created and merged within 4 days.
• Notable: Enhances project visibility by linking to educational content.

PR #54: Move wiki contents into doc folder, update references

• Summary: Consolidates documentation into the doc folder and updates references accordingly.
• Status: Created and merged within 4 days.
• Notable: Important for organizing documentation in a central location.

PR #53: Replace old graphics with new ones

• Summary: Updates graphics used in the repository with new versions.
• Status: Created and merged within 4 days.
• Notable: Visual updates can help improve understanding of project architecture or setup processes.

PR #52: Update HF Azure ML deployment managed compute to allow liveness probe configuration

• Summary: Modifies Azure ML deployment script to enable passing liveness probe configuration settings as an environment variable. Adds troubleshooting steps for ResourceNotReady error in HF Azure ML model endpoint guide.
• Status: Created and merged within 4 days.
• Notable: Addresses deployment issues with larger models on Azure ML managed compute which is critical for users deploying such models.

Summary

Recent pull requests show active development and maintenance of the project. There is good responsiveness from contributors and maintainers alike. The closure of pull requests without merging (like PR #62) indicates effective communication among contributors to avoid duplicate work. The discussions around versioning (PR #58) highlight an area that may require further attention to ensure users can easily navigate different versions of notebooks corresponding to different versions of the package. Overall, there seems to be a focus on improving user experience, whether through better documentation (PRs #57, #59), fixing bugs (PRs #60, #66), or enhancing usability (PRs #52, #63).

Report On: Fetch commits

Overview of the Project

The Python Risk Identification Tool for generative AI (PyRIT) is a framework designed to assist security professionals and machine learning engineers in evaluating the robustness of large language model (LLM) endpoints. It focuses on identifying various harm categories such as fabrication, misuse, prohibited content, security harms (e.g., malware generation), and privacy harms (e.g., identity theft). PyRIT is developed by the AI Red Team and aims to automate red teaming tasks to allow researchers to focus on more complex issues.

The tool is used for baseline assessments of models and inference pipelines, comparing these baselines with future iterations to track performance changes. It also aids in iterating and improving mitigations against different harms. Microsoft uses PyRIT to protect against prompt injection attacks.

Apparent Problems, Uncertainties, TODOs, or Anomalies

• The README file provides a good introduction and directs users to additional resources but does not mention any specific problems or TODOs within the project.
• There is an emphasis on ensuring that the use of Microsoft trademarks or logos does not cause confusion or imply Microsoft sponsorship, which suggests that branding guidelines are important for this project.
• The architecture diagram link points to an external GitHub repository, which could become a broken link if the file structure changes.

Recent Activities of the Development Team

Team Members and Recent Commits

• Gary (dlmgary): Fixed a misspelling in a variable name. This suggests attention to detail but is a minor change.
• Roman Lutz (romanlutz): Has been very active with multiple commits related to including datasets in the package, fixing hyperlinks, updating package versions, adding details to the contributor guide, moving wiki contents into the doc folder, updating documentation, and more. Roman's activity indicates a focus on improving documentation and usability of the tool.
• Raja Sekhar Rao Dheekonda (rdheekonda): Contributed to hyperlink fixes, added details about forking/PRs in the contribution guide, fixed issues with pypi package directory, cleaned up notebook outputs, updated Azure ML deployment compute configurations, and integrated Hugging Face Models with Azure ML among other things. Raja's contributions show involvement in both documentation and technical improvements.
• Nina Chikanov (nina-msft): Added details to the contributor guide about forking/PRs and converted the PyRIT Framework How to Guide to jupytext. Nina's work seems focused on community engagement and documentation.
• rlundeen2: Addressed several bugs related to pypi packaging and Azure ML integration, improved multi-turn strategy, added chat message normalizers for AML compatibility, standardized environments for completions and embeddings, among other tasks. This developer has been involved in both bug fixes and feature enhancements.

Patterns and Conclusions

• Collaboration: There is evidence of collaboration among team members with co-authorship noted in several commits.
• Documentation Focus: A significant portion of recent activity has been around improving documentation (e.g., moving wiki contents into doc folder), suggesting that making the project more accessible and user-friendly is currently a priority.
• Technical Improvements: Alongside documentation updates, there have been technical improvements such as bug fixes and feature enhancements indicating ongoing development and maintenance of the tool.
• Release Preparation: The activity in release branches like releases/v0.1.0 and releases/v0.1.1 by Roman Lutz suggests that the team is preparing for new releases of the software.

Conclusion

The development team behind PyRIT has been actively working on both improving documentation and addressing technical aspects of the project. The recent commits indicate a well-rounded approach to maintaining the software with attention given to user experience, technical robustness, and collaborative development practices.

Report On: Fetch Files For Assessment

The provided source code files and documentation are part of the PyRIT project, which is an automation framework designed to assist security professionals and ML engineers in red teaming foundation models and their applications. Below is an analysis of the structure and quality of the provided source code files and documentation:

General Observations

• The project follows a clear and organized structure, separating documentation, examples (demos), and source code logically.
• Documentation is comprehensive, covering installation, usage guides, deployment instructions, contributing guidelines, and more.
• The source code includes comments and docstrings where necessary, improving readability and maintainability.
• YAML files for defining attack strategies and prompt templates indicate a data-driven approach to configuration, enhancing flexibility.

Specific File Analysis

doc/setup/install_pyrit.md

• Structure: Markdown file with clear headings and concise instructions for installing PyRIT.
• Quality: High-quality documentation with straightforward steps. It could benefit from specifying supported operating systems or environments (e.g., Windows, Linux, macOS) if there are any specific considerations.

doc/how_to_guide.ipynb & doc/demo/1_gandalf.ipynb

• Structure: Jupyter notebooks that mix markdown explanations with executable Python code. They provide a step-by-step guide and demonstration of using PyRIT.
• Quality: The notebooks are well-documented with clear explanations. Code cells include comments for additional context. The choice of Jupyter Notebook format allows users to follow along interactively, which is excellent for learning.

pyrit/__init__.py

• Structure: Initialization file for the PyRIT package. Contains version information.
• Quality: Simple and clean. It adheres to standard practices for package initialization files. Including version information here is helpful for package management.

YAML Files (red_team_chatbot_with_objective.yaml, aim.yaml)

• Structure: YAML format used for defining configurations such as attack strategies and prompt templates.
• Quality: These files are well-structured and make use of YAML's hierarchical nature for clear definition of parameters and templates. The use of comments or descriptions within these files could enhance understanding for new contributors or users.

doc/deployment/deploy_hf_model_aml.ipynb

• Structure: A Jupyter notebook detailing the deployment process of models to Azure ML.
• Quality: This notebook appears to be comprehensive, covering prerequisites, environment setup, credentials configuration, model checking in registries, endpoint creation, and deployment. It serves as a thorough guide for deploying models with Azure ML but would benefit from validation checks or troubleshooting tips for common issues users might encounter.

Overall Assessment

The PyRIT project demonstrates high-quality software engineering practices with well-documented code, clear structure, and comprehensive guides. The use of Jupyter Notebooks for documentation is particularly effective for educational purposes, allowing users to learn through interactive examples. Improvements could include more detailed environment setup instructions (e.g., operating system considerations) and additional comments in YAML configuration files for clarity.