SSP-Bench: A Hybrid Data Generation Framework for Safety, Security, and Privacy Evaluation
Evaluation of large language models (LLMs) for safety, security, and privacy (SSP) relies heavily on static benchmarks, which suffer from score saturation, data contamination, and aggregation artifacts, and fail to capture sensitivity to linguistic variation. As a result, models that perform well on fixed test sets often fail under semantically equivalent rephrasings. We introduce SSP-Bench, a dynamic benchmarking framework that generates evaluation instances on demand while preserving domain consistency. The framework ensures label validity through externally grounded sources, enforces scope via service-specific validation, and calibrates difficulty using a multi-model steering panel. Be...
| Authors | Fatih Deniz, Yazan Boshmaf, Issa Khalil |
|---|---|
| Primary category | cs.CR |
| Published | 21 September 2026 |
| Last updated at source | 26 September 2026 |
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