arXiv Computation and Language October 1, 2026 By Parisa Salmani, Peter R. Lewis

Evaluating Language Model Safety Across Long Adversarial Conversations

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The paper investigates how conversational safety in language models degrades over extended, adversarial interactions. By testing three instruction‑tuned models with persistent adversarial users across up to 101 turns, the study finds that safe‑response rates drop sharply from 85–100% at the first turn to 15–44% by the end. This demonstrates that strong single‑turn safety does not guarantee continued safety in long conversations.

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