Robust Critics: Defending LLMs Against Multi-Turn Attacks
When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question? This ambiguity is one of the central challenges of LLM safety. A model that assumes the worst harms legitimate users; one that assumes the best is easily exploited. The problem is compounded in multi-turn dialogue, where an attacker's true intent may only reveal itself gradually across many exchanges, yet existing safety frameworks apply a contextual bandit treatment, ignoring the trajectory of the conversation. To that end, we propose Dialogue Critic Guided Sampling (DCGS), a framework that addresses this by inferring user intent at every turn of dialogue. Instead of ...
| Authors | Roman Belaire, Arunesh Sinha, Pradeep Varakantham |
|---|---|
| Primary category | cs.AI |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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