Azure's Python Risk Identification Tool for Generative AI (PyRIT) continues to evolve with significant enhancements aimed at improving dataset integration and documentation, despite some challenges in user navigation due to the absence of a dedicated website.
PyRIT serves as an automation framework designed to aid security professionals and machine learning engineers in identifying risks associated with generative AI systems. It focuses on assessing the robustness of large language model endpoints against various harm categories, including fabrication, misuse, and prohibited content.
Recent issues and pull requests (PRs) indicate a strong emphasis on expanding PyRIT’s functionality. Notable issues include the addition of new datasets (#367), improvements in documentation (#362), and enhancements to existing converters (#363). The presence of multiple "help wanted" labels suggests a need for community contributions to address these enhancements.
Roman Lutz (romanlutz)
Nina Chikanov (nina-msft)
jsong468
Volkan Kutal (KutalVolkan)
Santiago Zanella-Beguelin (s-zanella)
Summary
Quantified Reports
Detailed ReportsPromptTarget classes, contributing 67 changes across 5 files.The development team is actively engaged in both feature development and maintenance tasks, with a notable focus on enhancing various components within the PyRIT framework. Collaborative efforts are evident through multiple co-authored PRs.
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 6 | 2 | 6 | 0 | 1 |
| 30 Days | 12 | 7 | 21 | 2 | 1 |
| 90 Days | 29 | 20 | 64 | 10 | 1 |
| All Time | 54 | 35 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Roman Lutz |  |
1 | 2/2/0 | 2 | 71 | 1804 |
| Nina Chikanov |  |
1 | 0/1/0 | 1 | 42 | 1043 |
| jsong468 |  |
1 | 5/4/0 | 4 | 28 | 601 |
| Volkan Kutal |  |
1 | 2/2/0 | 2 | 7 | 476 |
| rlundeen2 |  |
1 | 3/2/0 | 2 | 9 | 227 |
| jbolor21 |  |
1 | 4/2/1 | 2 | 20 | 122 |
| Raja Sekhar Rao Dheekonda |  |
2 | 4/4/0 | 6 | 5 | 90 |
| saphirqi7 |  |
1 | 0/1/0 | 1 | 12 | 88 |
| Andrew Elgert |  |
1 | 0/1/0 | 1 | 4 | 79 |
| Santiago Zanella-Beguelin |  |
1 | 1/1/0 | 1 | 5 | 67 |
| Richard Edgar |  |
1 | 1/1/0 | 1 | 1 | 6 |
| Dan Jones (donebydan) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (gseetha04) | 0 | 1/0/1 | 0 | 0 | 0 | |
| None (deltaRed1a) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
The Azure/PyRIT project currently has 19 open issues, with recent activity primarily driven by Roman Lutz, who has created several feature requests and enhancements over the past week. Notably, many of the issues are labeled as "help wanted," indicating a potential need for community involvement or contributions. There is a clear focus on enhancing the project's capabilities, particularly in terms of dataset integration and documentation improvements.
Several issues stand out due to their implications for the project's future direction. For instance, the lack of a website for documentation (#362) suggests that users may struggle to navigate resources effectively. Additionally, the presence of multiple feature requests related to dataset support and enhancements indicates an ongoing effort to expand PyRIT's functionality, which could lead to increased user engagement and broader adoption.
Issue #367: FEAT add PKU-SafeRLHF dataset
Issue #365: FEAT explore default settings on targets used for generating red teaming prompts
Issue #363: FEAT Crossover and rephrase converters for fuzzer orchestrator
Issue #362: DOC Website for PyRIT
Issue #361: BUG PyTorch / TensorFlow warning
Issue #359: BUG Failure in CrescendoOrchestrator
Issue #355: FEAT add support for multimodal data from HarmBench
Issue #353: FEAT: Add Unify Integration for Multi-Provider LLM Support
Issue #347: FEAT Local Hugging Face model support
Issue #282: FEAT Metadata for datasets should allow fields as string OR list of strings
The recent issues reflect a strong emphasis on enhancing the functionality of PyRIT through new features and integrations, particularly concerning dataset support and improved documentation. This trend suggests a strategic focus on making the tool more versatile and user-friendly, which is crucial for attracting new users and contributors.
Moreover, the presence of multiple bug reports alongside feature requests indicates that while the project is evolving, it also faces challenges that need addressing to ensure stability and usability. The community's engagement in these discussions is vital for fostering collaboration and driving improvements.
Overall, the current state of GitHub issues highlights both opportunities for growth and areas requiring immediate attention to enhance user experience and project reliability.
The analysis of the pull requests (PRs) for the Azure/PyRIT project reveals a total of 9 open PRs, with significant contributions aimed at enhancing the functionality and robustness of the tool. The PRs cover a range of features, bug fixes, and improvements, reflecting an active development environment focused on addressing various aspects of risk assessment in generative AI systems.
PR #371: FEAT: Refusal Scorer
Created by: rlundeen2 (2 days ago)
This PR introduces a refusal scorer to predict whether a response is a refusal. It enhances Crescendo's functionality and improves retry logic for LLM scoring. Notable review comments suggest further elaboration on certain parameters and potential improvements in code readability.
PR #369: [DRAFT] FEAT: Handling duplicate memory for PromptRequestPiece/Score entries
Created by: jsong468 (4 days ago)
This draft PR addresses how to manage scores for duplicate PromptRequestPieces. It proposes a new field in the database to track original prompts, which could optimize memory usage. Review comments indicate a need for clarity on certain implementation details.
PR #360: FEAT: Fuzzer orchestrator
Created by: gseetha04 (6 days ago)
This PR adds a new orchestrator based on the GPTFuzzer paper, implementing an MCTS algorithm for seed selection. Review feedback includes suggestions for improving test cases and code structure.
PR #356: FEAT: Supporting the new tongue tied Gandalf levels
Created by: Dan Jones (donebydan) (12 days ago)
This PR adds support for new Gandalf levels requiring custom scoring logic. It includes tests and documentation but has raised questions regarding the necessity of certain parameters.
PR #354: [DRAFT] FEAT: optimized huggingface model support
Created by: Volkan Kutal (KutalVolkan) (16 days ago)
A draft PR aimed at reintroducing HuggingFace model support with optimizations for loading times. It outlines future work on model management and performance profiling.
PR #351: [DRAFT] FEAT: Adding new HTTP Target
Created by: jbolor21 (18 days ago)
This draft PR proposes adding an HTTP target for endpoints without API access. Review comments suggest improvements in code consistency and handling request types.
PR #314: FEAT emoji jailbreak
Created by: Roman Lutz (romanlutz) (44 days ago)
This PR introduces an emoji-based jailbreak mechanism. Review discussions highlight preferences for implementation details regarding text encoding.
PR #276: [DRAFT] FEAT Update Data Type Serializer Functionality to Support MultiModal
Created by: Andrew Elgert (elgertam) (66 days ago)
A draft PR aiming to enhance data type serialization for multimodal capabilities. Review comments suggest further validation checks and potential async implementations.
PR #180: FEAT Add adversarial suffix attack GCG
Created by: NaijingGuo (139 days ago)
This PR introduces an optimizer for adversarial suffix attacks based on GCG methodology. Review discussions focus on structuring the implementation into classes for better usability.
The current set of open pull requests reflects a diverse range of enhancements and features being actively developed within the Azure/PyRIT project. A few notable themes emerge from this analysis:
Feature Enhancements: Many PRs focus on adding new functionalities, such as the refusal scorer (#371), fuzzer orchestrator (#360), and support for new Gandalf levels (#356). These enhancements indicate an ongoing effort to expand the capabilities of PyRIT in assessing risks associated with generative AI models.
Memory Management Improvements: Several PRs address memory management issues, particularly concerning duplicate entries (#369). The introduction of fields to track original prompts suggests a thoughtful approach to optimizing resource usage while maintaining data integrity.
Community Engagement: The review comments across various PRs demonstrate active engagement among contributors, with suggestions aimed at improving code quality, readability, and overall design. This collaborative environment fosters continuous improvement and innovation within the project.
Testing and Documentation: Many open PRs emphasize the importance of testing, with contributors adding unit tests alongside their features (#371, #356). However, there are also instances where documentation updates are noted as pending or lacking (#354). Ensuring comprehensive documentation is crucial for maintaining usability as features evolve.
Draft Status: A couple of PRs remain in draft status (#369, #354), indicating that contributors are still refining their implementations or awaiting further feedback before finalizing their contributions. This iterative process is essential in open-source projects to ensure quality before merging changes into the main branch.
Recent Activity: The recent activity across these pull requests indicates that the project is actively maintained, with contributors addressing both new feature requests and existing issues promptly. However, it is worth noting that some older PRs have not seen recent activity or resolution, which could lead to stagnation if not addressed.
In conclusion, the Azure/PyRIT project showcases a vibrant development community focused on enhancing risk assessment tools for generative AI systems. The ongoing contributions reflect a commitment to improving functionality while ensuring robust memory management and thorough testing practices are upheld.
Santiago Zanella-Beguelin (s-zanella)
top_p argument in various PromptTarget classes, contributing to 67 changes across 5 files.Nina Chikanov (nina-msft)
jsong468
Richard Edgar (riedgar-ms)
OllamaChatTarget, contributing minor changes (6 changes across 1 file).Andrew Elgert (elgertam)
Volkan Kutal (KutalVolkan)
saphirqi7
Raja Sekhar Rao Dheekonda (rdheekonda)
Roman Lutz (romanlutz)
jbolor21
rlundeen2
The recent activities of the development team demonstrate a robust commitment to enhancing the PyRIT project through collaborative efforts focused on both new feature implementation and ongoing maintenance. The diversity of contributions reflects a well-rounded approach to project development, ensuring that both innovation and reliability are prioritized as the project evolves.