NVIDIA/garak
Nov 2024 – Sep 2025
9 Months active
Languages Used
PythonMarkdownJSONRSTrstJinjaYAMLCSS
Technical Skills
Code RefactoringDetector ImplementationLLM SecurityProbe ConfigurationSecurity TestingVulnerability Detection
Leon Derczynski
PROFILE
Leon Derczynski
Lars Derczynski contributed to the NVIDIA/garak repository by engineering features and fixes that enhanced vulnerability detection, reporting, and configuration management for language model security assessment. Over nine months, Lars developed and refined detectors and probes for ANSI escape sequences, improved prompt injection and leetspeak exploitation coverage, and modernized integrations with frameworks like OpenAI and Hugging Face. Using Python, CSS, and YAML, Lars focused on modularity, robust error handling, and test-driven development, ensuring maintainable code and reliable analysis. His work included standardizing reporting terminology, strengthening documentation and UI, and decoupling configuration logic, resulting in deeper, more actionable security insights.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
69Total
Bugs
9
Commits
69
Features
22
Lines of code
1,738
Activity Months9
Your Network
1573 peopleSame Organization
@nvidia.com1525
Work History
September 2025
9 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for NVIDIA/garak focused on security-oriented tokenizer enhancements and documentation/ui improvements. Implemented a Hugging Face tokenizer ANSI escape detector and integrated an HF tokenizer probe with a standardized access point and a dedicated ANSI data module. Refactored to avoid tokenizer double-loading and updated tests to reflect new data layout. Concurrently delivered comprehensive documentation improvements and a refreshed UI/theme to improve developer experience and onboarding.
9 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for NVIDIA/garak focused on security-oriented tokenizer enhancements and documentation/ui improvements. Implemented a Hugging Face tokenizer ANSI escape detector and integrated an HF tokenizer probe with a standardized access point and a dedicated ANSI data module. Refactored to avoid tokenizer double-loading and updated tests to reflect new data layout. Concurrently delivered comprehensive documentation improvements and a refreshed UI/theme to improve developer experience and onboarding.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 20252025-08 monthly summary focused on standardizing Garak's reporting terminology to improve clarity and consistency across evaluator outputs and analysis comments. Implemented naming changes to align metrics with business language and downstream analytics. No major defects reported this month; effort centered on feature refinement and code hygiene to enable reliable reporting and easier onboarding.
August 2025
2 Commits • 1 Features
Aug 1, 20252025-08 monthly summary focused on standardizing Garak's reporting terminology to improve clarity and consistency across evaluator outputs and analysis comments. Implemented naming changes to align metrics with business language and downstream analytics. No major defects reported this month; effort centered on feature refinement and code hygiene to enable reliable reporting and easier onboarding.
July 2025
3 Commits • 1 Features
Jul 1, 2025July 2025 — NVIDIA/garak: Focused on strengthening test coverage for prompt/template processing and correcting docs formatting. Key features delivered: Template and prompt processing tests in probes.doctor to enforce template integrity and clean final prompts, with improved test output visibility. Major bugs fixed: Documentation formatting fix in docs to correct a missing equals sign and ensure consistent rendering. Overall impact: Increased reliability of prompt templates, faster debugging through clearer test outputs, and improved documentation quality, contributing to lower support overhead and smoother integration for downstream users. Technologies/skills demonstrated: test-driven development, test automation, Python-based test suites, and documentation hygiene.
3 Commits • 1 Features
Jul 1, 2025July 2025 — NVIDIA/garak: Focused on strengthening test coverage for prompt/template processing and correcting docs formatting. Key features delivered: Template and prompt processing tests in probes.doctor to enforce template integrity and clean final prompts, with improved test output visibility. Major bugs fixed: Documentation formatting fix in docs to correct a missing equals sign and ensure consistent rendering. Overall impact: Increased reliability of prompt templates, faster debugging through clearer test outputs, and improved documentation quality, contributing to lower support overhead and smoother integration for downstream users. Technologies/skills demonstrated: test-driven development, test automation, Python-based test suites, and documentation hygiene.
July 2025
May 2025
4 Commits • 1 Features
May 1, 2025May 2025 (NVIDIA/garak): Enhanced Garak reporting flexibility and analysis reliability. Key features delivered include a configurable option to show/hide group scores in Garak HTML reports, with decoupled calculation from display and standardized terminology for group-level items. This was implemented through commits: c9ac997ad418f89199ddd55301b9a643598f259b (add option for no group score); 11b13dae049451dd280837838274ac0c8cd98a16 (decouple group score calc from group score display); and b4a12cb6eeb41aafef2a67cce278c69129547d36 (fix formatting, rename module->group for group-level items). Major bug fix included relaxing the failure detection threshold in Garak analysis to 0.041 to tolerate a single failure in the lowest prompt count bag probe without triggering TERRIBLE, via commit e099291dad9c09fd341e9afe2b2ecca8aed8fada (bump min s.d.).
May 2025
4 Commits • 1 Features
May 1, 2025May 2025 (NVIDIA/garak): Enhanced Garak reporting flexibility and analysis reliability. Key features delivered include a configurable option to show/hide group scores in Garak HTML reports, with decoupled calculation from display and standardized terminology for group-level items. This was implemented through commits: c9ac997ad418f89199ddd55301b9a643598f259b (add option for no group score); 11b13dae049451dd280837838274ac0c8cd98a16 (decouple group score calc from group score display); and b4a12cb6eeb41aafef2a67cce278c69129547d36 (fix formatting, rename module->group for group-level items). Major bug fix included relaxing the failure detection threshold in Garak analysis to 0.041 to tolerate a single failure in the lowest prompt count bag probe without triggering TERRIBLE, via commit e099291dad9c09fd341e9afe2b2ecca8aed8fada (bump min s.d.).
April 2025
17 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for NVIDIA/garak: Delivered stronger detection capabilities, expanded exploit detection coverage, and cleaned up terminology and configuration for long-term maintainability. Demonstrated rigorous testing, clearerDocumentation, and proactive risk mitigation.
17 Commits • 3 Features
Apr 1, 2025April 2025 monthly summary for NVIDIA/garak: Delivered stronger detection capabilities, expanded exploit detection coverage, and cleaned up terminology and configuration for long-term maintainability. Demonstrated rigorous testing, clearerDocumentation, and proactive risk mitigation.
April 2025
March 2025
5 Commits • 2 Features
Mar 1, 2025March 2025 highlights for NVIDIA/garak: Delivered robustness and accuracy enhancements across configuration handling and encoding detection, while strengthening guardrails around resource usage. Key improvements include enforcement of max worker limits with full configuration validation and improved user guidance; guards for missing triggers and language tag standardization to broaden encoding detection applicability; and an internal n-gram matching refactor to ensure reliable detection by deriving n from local scope. These changes reduce misconfig-driven failures, improve reliability under load, and enhance developer and user experience. Technical skills demonstrated include testing, refactoring for local scope, defensive programming, and clear error messaging.
March 2025
5 Commits • 2 Features
Mar 1, 2025March 2025 highlights for NVIDIA/garak: Delivered robustness and accuracy enhancements across configuration handling and encoding detection, while strengthening guardrails around resource usage. Key improvements include enforcement of max worker limits with full configuration validation and improved user guidance; guards for missing triggers and language tag standardization to broaden encoding detection applicability; and an internal n-gram matching refactor to ensure reliable detection by deriving n from local scope. These changes reduce misconfig-driven failures, improve reliability under load, and enhance developer and user experience. Technical skills demonstrated include testing, refactoring for local scope, defensive programming, and clear error messaging.
February 2025
25 Commits • 9 Features
Feb 1, 2025February 2025 NVIDIA/garak monthly summary: Turn-based migration across core components and OpenAI-related modules with added typechecking; cross-framework modernization migrating Litellm, Llava, LangChain, and Cohere; JSON test data validity fixes and expanded reliability tests (probes, visual jailbreak, vision tests); nim.Vision migration with image loading tests and Ollama integration migrated to Turn; improved error handling and OpenAI integration refinements (not-found errors, message scope, litellm exception patterns). These changes reduce maintenance burden, enable safer extensibility, improve test stability, and accelerate feature delivery for AI-enabled workflows.
25 Commits • 9 Features
Feb 1, 2025February 2025 NVIDIA/garak monthly summary: Turn-based migration across core components and OpenAI-related modules with added typechecking; cross-framework modernization migrating Litellm, Llava, LangChain, and Cohere; JSON test data validity fixes and expanded reliability tests (probes, visual jailbreak, vision tests); nim.Vision migration with image loading tests and Ollama integration migrated to Turn; improved error handling and OpenAI integration refinements (not-found errors, message scope, litellm exception patterns). These changes reduce maintenance burden, enable safer extensibility, improve test stability, and accelerate feature delivery for AI-enabled workflows.
February 2025
December 2024
2 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for NVIDIA/garak focusing on telemetry accuracy, documentation clarity, and alignment with external service endpoints. Key features delivered include updating Pepy download badges to reflect static.pepy.tech endpoints for total and monthly downloads, and refining the Garak README to clearly compare its probing capabilities to Nmap and Metasploit Framework, aligning expectations for users and stakeholders. These changes were implemented via commits 76b56fb5baf698ab2c61c0b4c6950662a1bfd9eb and 2ae18ae34723b297c2f798d2eb22fd33d112987e, respectively. Overall impact: improved accuracy of download metrics displayed in badges, reduced ambiguity in Garak's capabilities, and clearer communication for onboarding and external contributors. Technologies/skills demonstrated: documentation/readme craftsmanship, product messaging alignment, and integration awareness with external services (static.pepy.tech endpoints).
December 2024
2 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for NVIDIA/garak focusing on telemetry accuracy, documentation clarity, and alignment with external service endpoints. Key features delivered include updating Pepy download badges to reflect static.pepy.tech endpoints for total and monthly downloads, and refining the Garak README to clearly compare its probing capabilities to Nmap and Metasploit Framework, aligning expectations for users and stakeholders. These changes were implemented via commits 76b56fb5baf698ab2c61c0b4c6950662a1bfd9eb and 2ae18ae34723b297c2f798d2eb22fd33d112987e, respectively. Overall impact: improved accuracy of download metrics displayed in badges, reduced ambiguity in Garak's capabilities, and clearer communication for onboarding and external contributors. Technologies/skills demonstrated: documentation/readme craftsmanship, product messaging alignment, and integration awareness with external services (static.pepy.tech endpoints).
November 2024
2 Commits • 1 Features
Nov 1, 2024In 2024-11, delivered a focused feature set for NVIDIA/garak to strengthen vulnerability detection using language models by adding ANSI escape sequence detection and probing capabilities. Implemented detectors to identify ANSI sequences (AnsiEscapeEscaped, AnsiEscapeRaw) and a probe (AnsiEscaped) to elicit such sequences from models for vulnerability assessment. Refined the feature by renaming detector classes for consistency, updating the recommended detector reference in the probe, and aligning prompts attributes for reliability.
2 Commits • 1 Features
Nov 1, 2024In 2024-11, delivered a focused feature set for NVIDIA/garak to strengthen vulnerability detection using language models by adding ANSI escape sequence detection and probing capabilities. Implemented detectors to identify ANSI sequences (AnsiEscapeEscaped, AnsiEscapeRaw) and a probe (AnsiEscaped) to elicit such sequences from models for vulnerability assessment. Refined the feature by renaming detector classes for consistency, updating the recommended detector reference in the probe, and aligning prompts attributes for reliability.
November 2024
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Quality Metrics
Correctness92.4%
Maintainability93.0%
Architecture87.6%
Performance86.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSSJSONJinjaMarkdownPythonRSTYAMLrst
Technical Skills
API IntegrationBackend DevelopmentCI/CDCLI DevelopmentCSSCode AnalysisCode OrganizationCode ReadabilityCode RefactoringCommand Line Interface (CLI)ConfigurationConfiguration ManagementData ManagementData ValidationDebugging
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