6 citations · 18 across the 13 of their papers we have counts for
6 papers · 1 filter
Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs
Mark Russinovich, Blake Bullwinkel, Giorgio Severi +2
Language model safety is typically evaluated one interaction at a time. We show that a weaker, unaligned model can split a harmful task into benign-looking subproblems, consult a s…
Evading Chain-of-Thought Monitoring Through Model Poisoning
Giorgio Severi, Shujaat Mirza, Blake Bullwinkel +1
Chain-of-thought (CoT) monitoring is an increasingly important component of AI safety stacks but relies on the assumption that a model's reasoning trace is informative about its ac…
Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO
Blake Bullwinkel, Eugenia Kim, Amanda Minnich +1
Language model safety must continually adapt to evolving attacks. Recent works have demonstrated that reinforcement learning can be used to train stronger attacker and defender mod…
GRP-Obliteration: Unaligning LLMs With a Single Unlabeled Prompt
Mark Russinovich, Yanan Cai, Keegan Hines +3
Safety alignment is only as robust as its weakest failure mode. Despite extensive work on safety post-training, it has been shown that models can be readily unaligned through post-…
The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers
Blake Bullwinkel, Giorgio Severi, Keegan Hines +3
Detecting whether a model has been poisoned is a longstanding problem in AI security. In this work, we present a practical scanner for identifying sleeper agent-style backdoors in…
Beyond the Single Turn: Reframing Refusals as Dynamic Experiences Embedded in the Context of Mental Health Support Interactions with LLMs
Ningjing Tang, Alice Qian, Qiaosi Wang +6
Content Warning: This paper contains participant quotes and discussions related to mental health challenges, emotional distress, and suicidal ideation. Large language models (LLMs)…