בדיקת אמינות דגלי שפה במגע רציף עם משתמש פוגעני

תקציר מקורי באנגליתarXiv:2609.38357v1 Announce Type: new Abstract: Conversational safety evaluations often test language models with a single harmful prompt, even though real-world systems interact with users through long, adaptive conversations. This study examines whether models continue to respond safely when an adversarial user persists across multiple turns. We evaluate three open-weight, instruction-tuned models on two harmful prompts across different conversation lengths and random seeds. In each setting, a second language model acts as a persistent adversarial user, while a safety classifier labels every response as safe or unsafe. Across all model-prompt combinations, first-turn safe-response rates ranged from 85% to 100%. By depth 11, they dropped to 38-61%, and by depth 101, to 15-44%. This declin