AI RESEARCH
Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications
arXiv CS.AI
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ArXi:2605.24883v1 Announce Type: new The widespread integration of Large Language Models (LLMs) necessitates rigorous and systematic safety evaluation. Existing paradigms either rely on constructed benchmarks to assess safety from predefined perspectives, or employ dynamic red-teaming to probe potential vulnerabilities. While effective, these approaches face challenges, as they depend heavily on expert domain knowledge, offer limited systematic guarantees, and are vulnerable to rapid obsolescence. To address these limitations, we.