NVIDIA/garak
Aug 2025 – Oct 2025
3 Months active
Languages Used
PythonRSTTOMLTexttextMarkdownpython
Technical Skills
Backend DevelopmentCode RefactoringConfiguration ManagementData ValidationDependency ManagementDocumentation
Patricia Pampanelli
PROFILE
Patricia Pampanelli
Over a three-month period, Paolo Pampanelli engineered foundational security and maintainability features for the NVIDIA/garak repository, focusing on adversarial probe development and architectural modernization. He delivered the Disguise and Reconstruction Attack (DRA) probe, introducing randomized templates, caching, and tiered risk levels to simulate and test LLM vulnerabilities. Paolo refactored core components using Python, implementing abstract base classes and decoupling dependencies through lazy loading to improve modularity and runtime efficiency. His work included comprehensive documentation, expanded test coverage, and reproducibility improvements, resulting in a more robust, scalable backend that supports safer, business-ready LLM security testing and streamlined future integrations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
39Total
Bugs
0
Commits
39
Features
7
Lines of code
1,998
Activity Months3
Your Network
1390 people
Work History
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
Correctness90.2%
Maintainability93.6%
Architecture90.8%
Performance84.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonRSTTOMLTextpythontext
Technical Skills
API DesignAbstract Base ClassesAbstract ClassesBackend DevelopmentCache ManagementCode FormattingCode OptimizationCode RefactoringConfiguration ManagementData ValidationDebuggingDependency ManagementDocumentationFile ManagementLLM Security
Repositories Contributed To
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