NVIDIA/garak
Aug 2025 – Feb 2026
7 Months active
Languages Used
PythonRSTTOMLTexttextMarkdownpythonJSON
Technical Skills
Backend DevelopmentCode RefactoringConfiguration ManagementData ValidationDependency ManagementDocumentation
Patricia Pampanelli
PROFILE
Patricia Pampanelli
Worked on NVIDIA/garak, delivering security probes, configuration loaders, and evaluation metrics to strengthen LLM safety and moderation pipelines. Developed the Disguise and Reconstruction Attack probe with randomized behaviors and caching, and modernized probe workflows for maintainability. Overhauled detector and probe architecture using Python, introducing abstract base classes and centralized configuration defaults. Enhanced configuration loading with robust JSON and YAML support, improved error handling, and comprehensive test coverage. Integrated F1 metrics and descriptive labeling for detectors, supporting reproducible evaluation and actionable insights. Emphasized documentation and statistical methods, ensuring clarity and reliability for future detector deployments and plugin integrations within the repository.
Overall Statistics
Feature vs Bugs
93%Features
Repository Contributions
74Total
Bugs
2
Commits
74
Features
25
Lines of code
4,273
Activity Months7
Your Network
1685 peopleSame Organization
@nvidia.com1629
Shared Repositories
56
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Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for NVIDIA/garak focused on documenting evaluation methodology improvements for string matching detectors and enhancing measurement reproducibility. The key change clarifies how detectors are evaluated and includes a bootstrap statistics reference to support statistical rigor and bootstrap-based decision-making. This work is captured in a single, well-traced commit and strengthens documentation for future detector deployments.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for NVIDIA/garak focused on documenting evaluation methodology improvements for string matching detectors and enhancing measurement reproducibility. The key change clarifies how detectors are evaluated and includes a bootstrap statistics reference to support statistical rigor and bootstrap-based decision-making. This work is captured in a single, well-traced commit and strengthens documentation for future detector deployments.
February 2026
January 2026
23 Commits • 14 Features
Jan 1, 2026January 2026 performance summary for NVIDIA/garak. Focused on stabilizing configuration handling, expanding detector evaluation and labeling capabilities, and strengthening code quality and metrics integration. Delivered substantive improvements across detector labeling, evaluation metrics, and robustness of config loading, driving reliability and actionable performance insights for moderation pipelines.
January 2026
23 Commits • 14 Features
Jan 1, 2026January 2026 performance summary for NVIDIA/garak. Focused on stabilizing configuration handling, expanding detector evaluation and labeling capabilities, and strengthening code quality and metrics integration. Delivered substantive improvements across detector labeling, evaluation metrics, and robustness of config loading, driving reliability and actionable performance insights for moderation pipelines.
December 2025
6 Commits • 2 Features
Dec 1, 2025December 2025 - NVIDIA/garak: Delivered configuration loading enhancements and QA improvements to boost startup reliability and config versatility. Implemented JSON-first parsing, YAML/.yml extension support, and case-insensitive extensions with improved error handling and logging. Strengthened tests with isolated fixtures, expanding JSON/YAML coverage and reducing flaky tests.
6 Commits • 2 Features
Dec 1, 2025December 2025 - NVIDIA/garak: Delivered configuration loading enhancements and QA improvements to boost startup reliability and config versatility. Implemented JSON-first parsing, YAML/.yml extension support, and case-insensitive extensions with improved error handling and logging. Strengthened tests with isolated fixtures, expanding JSON/YAML coverage and reducing flaky tests.
December 2025
November 2025
5 Commits • 1 Features
Nov 1, 2025For NVIDIA/garak in 2025-11, delivered a robust multi-format configuration loader with JSON support, enhanced extension-less lookup for JSON configs, and comprehensive test coverage, while performing maintenance cleanup and clarifying usage rules. The changes improve configurability, reliability, and developer velocity across environments that rely on YAML and JSON for configuration.
November 2025
5 Commits • 1 Features
Nov 1, 2025For NVIDIA/garak in 2025-11, delivered a robust multi-format configuration loader with JSON support, enhanced extension-less lookup for JSON configs, and comprehensive test coverage, while performing maintenance cleanup and clarifying usage rules. The changes improve configurability, reliability, and developer velocity across environments that rely on YAML and JSON for configuration.
October 2025
10 Commits • 2 Features
Oct 1, 2025October 2025 performance summary for NVIDIA/garak: Delivered a foundational overhaul of Garak's detector and probe architecture, enabling cleaner abstractions, stronger contracts, and centralized defaults. Implemented a detector configuration overhaul with improved cache/docs/tests, migrating to a primary/extended detector model and deprecating the old workflow. These changes reduce risk, improve maintainability, and set the stage for scalable detector integrations.
10 Commits • 2 Features
Oct 1, 2025October 2025 performance summary for NVIDIA/garak: Delivered a foundational overhaul of Garak's detector and probe architecture, enabling cleaner abstractions, stronger contracts, and centralized defaults. Implemented a detector configuration overhaul with improved cache/docs/tests, migrating to a primary/extended detector model and deprecating the old workflow. These changes reduce risk, improve maintainability, and set the stage for scalable detector integrations.
October 2025
September 2025
17 Commits • 4 Features
Sep 1, 2025September 2025 performance highlights for NVIDIA/garak: major DRA safety probe enhancements, modernization of Garak probe workflow, and a safer data pipeline through Detoxify integration. The work emphasizes reliability, reproducibility, maintainability, and business-ready safety features.
September 2025
17 Commits • 4 Features
Sep 1, 2025September 2025 performance highlights for NVIDIA/garak: major DRA safety probe enhancements, modernization of Garak probe workflow, and a safer data pipeline through Detoxify integration. The work emphasizes reliability, reproducibility, maintainability, and business-ready safety features.
August 2025
12 Commits • 1 Features
Aug 1, 2025Month: 2025-08 | NVIDIA/garak — Key security testing advancements and maintainability improvements. Delivered the Disguise and Reconstruction Attack (DRA) probe for Garak, including DRAFull and mini variants, with randomized templates/behaviors, caching, and tagging. Expanded test coverage and documentation to accompany the new probe, and introduced tiering to support multiple risk levels. Reworked the detoxify dependency by removing hard coupling and implementing lazy import, reducing runtime overhead and enabling safer future refactors. This work enhances Garak’s ability to simulate and test adversarial instruction scenarios, improving defense posture, reducing risk in LLM interactions, and accelerating security feedback loops for stakeholders.
12 Commits • 1 Features
Aug 1, 2025Month: 2025-08 | NVIDIA/garak — Key security testing advancements and maintainability improvements. Delivered the Disguise and Reconstruction Attack (DRA) probe for Garak, including DRAFull and mini variants, with randomized templates/behaviors, caching, and tagging. Expanded test coverage and documentation to accompany the new probe, and introduced tiering to support multiple risk levels. Reworked the detoxify dependency by removing hard coupling and implementing lazy import, reducing runtime overhead and enabling safer future refactors. This work enhances Garak’s ability to simulate and test adversarial instruction scenarios, improving defense posture, reducing risk in LLM interactions, and accelerating security feedback loops for stakeholders.
August 2025
Activity
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Quality Metrics
Correctness94.0%
Maintainability94.2%
Architecture92.8%
Performance88.8%
AI Usage21.0%
Skills & Technologies
Programming Languages
JSONMarkdownPythonRSTTOMLTextpythonreStructuredTexttext
Technical Skills
AI DevelopmentAI SafetyAI metricsAPI DesignAPI DevelopmentAbstract Base ClassesAbstract ClassesBackend DevelopmentCache ManagementCode FormattingCode OptimizationCode RefactoringConfiguration ManagementData ValidationDebugging
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