PurpleLlama, a project under the meta-llama umbrella, aims to enhance the security of large language models (LLMs) by integrating offensive and defensive cybersecurity strategies. The project has seen significant activity focused on documentation updates and security feature enhancements, reflecting ongoing efforts to address AI safety concerns.
The recent issues and pull requests indicate a concentrated effort on improving documentation and refining security measures. Notably, closed issues such as #44 and #37 highlight user inquiries about dataset usage and Llama Guard's rule effectiveness, pointing to active community engagement. The development team has been prolific, with members like Jianfeng Chi and Ujjwal Karn focusing on citation accuracy, while Dhaval Kapil contributed numerous examples of vulnerabilities such as UseAfterFree. This collaborative approach underscores a commitment to both transparency and security enhancement.
Overall, PurpleLlama is actively advancing its mission to secure generative AI through comprehensive documentation improvements and targeted security feature developments.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Cyrus Nikolaidis |  |
1 | 0/0/0 | 6 | 7 | 2503 |
| Dhaval Kapil |  |
1 | 0/0/0 | 8 | 45 | 2384 |
| Daniel Song |  |
1 | 0/0/0 | 12 | 44 | 2090 |
| Daniel Song |  |
1 | 0/0/0 | 4 | 95 | 1713 |
| Jianfeng Chi |  |
3 | 2/0/2 | 11 | 7 | 895 |
| Vlad Ionescu |  |
1 | 0/0/0 | 4 | 7 | 410 |
| an onion |  |
1 | 0/0/0 | 5 | 2 | 393 |
| Ujjwal Karn |  |
2 | 1/0/1 | 4 | 1 | 38 |
| Shengye Wan |  |
1 | 0/0/0 | 1 | 1 | 2 |
| Kate Plawiak |  |
1 | 0/0/0 | 1 | 1 | 2 |
| Thomas Robinson |  |
1 | 1/0/1 | 1 | 1 | 2 |
| Joseph Spisak (jspisak) | 0 | 0/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 0 | 1 | 0 | 0 | 0 |
| 30 Days | 2 | 9 | 2 | 1 | 1 |
| 90 Days | 9 | 13 | 10 | 6 | 1 |
| All Time | 27 | 27 | - | - | - |
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.
The PurpleLlama project has seen a total of 27 closed issues, with no open issues currently reported. Recent activity indicates a healthy engagement from the community, particularly around contributions to cybersecurity benchmarks and inquiries about model functionalities. Notably, several issues revolve around the integration and effectiveness of security measures in the Llama Guard model, suggesting ongoing refinement and user interest in enhancing its capabilities.
Several themes emerge from the closed issues: requests for clarification on dataset usage, inquiries about the effectiveness of custom rules in Llama Guard, and discussions about the precision of cybersecurity evaluations. A recurring concern is the need for better documentation and examples regarding the implementation of features, which could indicate potential barriers for new contributors or users.
Issue #44: CybersecurityBenchmarks | Request for Open Source Code Dataset and Clarification on Regex Rule Creation for ICD
Issue #47: write a request letter
Issue #43: Edited due to containing links.
Issue #42: How to convert Meta-Llama-Guard-2-8B to pre_trained models
Issue #41: Inquiry about risk assessment for assistant responses in Llama Guard 2
Issue #40: as
Issue #39: Edited due to containing links.
Issue #37: Llama-Guard2 doesn't respect custom rules, returns a single violated category even if multiple are violated.
Issue #36: Why there are two folders of insecure_code_detector?
Issue #35: insecure_code_detector.cli doesn't detect insecure code as expected.
The issues reflect a mix of user inquiries about functionality and technical challenges faced when using the tools provided by PurpleLlama. The most pressing concerns appear to be related to the efficacy of the Llama Guard model in detecting various categories of unsafe content and the usability of datasets for testing purposes.
The repository meta-llama/PurpleLlama has a total of 21 closed pull requests, with no open pull requests at the moment. The closed pull requests primarily focus on documentation updates, bug fixes, and enhancements related to security measures for large language models.
PR #49: Add citation to MODEL_CARD.md
Closed 11 days ago. This PR added a citation to the model card documentation, enhancing the project's transparency regarding its sources.
PR #48: [LG3] Add citation to README.md
Closed 12 days ago. This PR added a citation pointing to the Llama 3 paper in the README, which is significant for academic and practical references.
PR #46: Update MODEL_CARD.md
Closed 20 days ago. This PR updated the model card with a new description (S14) related to code interpreter abuse, which is crucial for understanding potential misuse of the model.
PR #45: Update PromptGuard README.md to fix broken link to promptguard tutorial
Closed 23 days ago. This PR fixed a broken link in the documentation, improving user experience and access to resources.
PR #38: Update README.md
Closed 26 days ago. This PR aimed to add the Llama 3 license information but was put on hold due to synchronization issues between internal and external repositories.
PR #32: ref: replace if-else chain with dict lookup for response computation
Closed 114 days ago. This optimization improved code efficiency by avoiding unnecessary creation of language models when not specified.
PR #30: update 2nd run result
Closed 120 days ago. This PR included numerous updates related to results from benchmarks, indicating ongoing evaluation efforts.
PR #29: Update MODEL_CARD.md
Closed 119 days ago. This PR addressed a broken link in the model card, ensuring users have accurate information.
PR #28: Update README.md
Closed 120 days ago. This PR updated several URLs and fixed broken links in the documentation.
PR #26: Set the execute bit on the download.sh script (Llama-Guard2)
Closed 122 days ago. This PR ensured that a crucial script could be executed as intended by users.
PR #25: Update README.md
Closed 122 days ago. A minor typo fix that contributes to overall documentation quality.
PR #24: Re-sync with internal repository
Merged 122 days ago. This PR aimed to synchronize changes between internal and external repositories, indicating active maintenance of project integrity.
PR #22: Set execute permissions on download script
Closed 135 days ago. Similar to PR #26, this ensured that scripts could be executed correctly by users.
PR #17: [trivial] mark download.sh as executable
Merged 136 days ago. A trivial but necessary change that resolved execution permission issues for users.
PR #9: Update README.md files
Merged 135 days ago. Comprehensive updates were made for clarity and consistency across multiple README files.
16-21. PRs #6, #5, #4, #3, #2, and #1: Various updates primarily focused on fixing typos and minor documentation improvements over several months leading up to their closure.
The collection of pull requests for meta-llama/PurpleLlama reflects a strong emphasis on improving documentation and ensuring accuracy in references related to cybersecurity evaluations of large language models (LLMs). The majority of these closed pull requests are focused on updating various markdown files (README.md and MODEL_CARD.md), which indicates an ongoing effort to maintain high-quality documentation that is critical for user engagement and understanding of the project's objectives.
Notably, there are several instances where contributors addressed broken links or citations within the documentation (e.g., PRs #48, #45, and #29). These updates are essential not only for user experience but also for maintaining academic integrity as they relate directly to the project's credibility in the research community.
The presence of multiple pull requests aimed at optimizing code performance (such as PR #32) showcases an active interest in enhancing the underlying software architecture alongside documentation improvements. This dual focus on both code quality and user-facing materials suggests a mature development process where contributors are aware of both technical and non-technical aspects of software delivery.
Additionally, there were instances where contributors faced challenges related to synchronization between internal and external repositories (e.g., PRs #38 and #24). Such issues highlight the complexities involved in managing open-source projects that may have ties to internal corporate structures, which can lead to delays or holds on certain contributions until alignment is achieved.
The overall trend in these pull requests indicates a proactive approach towards addressing potential vulnerabilities associated with LLMs through both technical enhancements and comprehensive documentation practices aimed at fostering responsible AI development. The focus on security measures such as those found in Llama Guard and Prompt Guard further emphasizes this commitment to safety in generative AI applications.
In conclusion, while there are no open pull requests currently indicating a stable state of contributions at this moment, the historical data reflects a robust engagement from contributors focused on enhancing both functionality and usability within the PurpleLlama project framework.
Jianfeng Chi (JFChi)
Summary
Quantified Reports
Detailed ReportsMODEL_CARD.md (11 days ago).README.md and MODEL_CARD.md multiple times for clarity and accuracy.Ujjwal Karn (ujjwalkarn)
README.md to match citations from the arXiv page (12 days ago).Vlad Ionescu (vladionescu)
An Onion (onionymous)
README.md.Dhaval Kapil (DhavalKapil)
UseAfterFree, HeapBufferOverflow).Cyrus Nikolaidis (cynikolai)
Daniel Song (dwjsong)
Kate Plawiak (kplawiak)
Simon Wan (SimonWan)
Joseph Spisak (jspisak)
Overall, the development team is demonstrating effective collaboration and a proactive approach towards enhancing both the functionality and documentation of the PurpleLlama project.