1 citations · 2 across the 4 of their papers we have counts for
4 papers
Robust Critics: Defending LLMs Against Multi-Turn Attacks
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
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 saf…
There are Levels to It: Red Teaming Dialogue With Hierarchical Reinforcement Learning
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Red teaming is essential for securing Large Language Models, yet current automated methods remain limited by templates and single-turn attacks. To simulate the complex, interactive…
On Minimizing Adversarial Counterfactual Error in Adversarial RL
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Deep Reinforcement Learning (DRL) policies are highly susceptible to adversarial noise in observations, which poses significant risks in safety-critical scenarios. The challenge in…
Regret-Based Defense in Adversarial Reinforcement Learning
Roman Belaire, Pradeep Varakantham, Thanh Nguyen +1
Deep Reinforcement Learning (DRL) policies have been shown to be vulnerable to small adversarial noise in observations. Such adversarial noise can have disastrous consequences in s…