benchmark-datasets

You can evaluate LLM robustness against jailbreak attacks by running the provided Python evaluation scripts against the benchmark catalog. This generates a benchmark_report.json containing reproducible safety, privacy, and bias assessment results for your models.

The best way to compare AI models for safety and compliance is to execute a unified benchmark suite covering cross-domain scenarios. Running the evaluation workflow yields a standardized security assessment report, enabling repeatable and consistent model comparisons.

Yes, you can add custom evaluation datasets to the security benchmark catalog by updating assets/benchmarks-catalog.yaml and references/BENCHMARK-CATALOG.md. This integrates new safety, robustness, or privacy benchmarks into your existing evaluation workflow.

No, this benchmark evaluation workflow requires no external dependencies. It operates self-contained using the included Python scripts, benchmark catalog data, and reference files to ensure fully reproducible model security and robustness comparisons.

Standardized AI benchmark datasets cover safety, robustness, jailbreak, privacy, and bias evaluation scenarios. These curated cross-domain benchmarks enable comprehensive security assessments and repeatable comparisons across different AI systems.