4 citations · 10 across the 10 of their papers we have counts for
4 papers · 1 filter
From Narrow Unlearning to Emergent Misalignment: Causes, Consequences, and Containment in LLMs
Erum Mushtaq, Anil Ramakrishna, Satyapriya Krishna +5
Recent work has shown that fine-tuning on insecure code data can trigger an emergent misalignment (EMA) phenomenon, where models generate malicious responses even to prompts unrela…
D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models
Satyapriya Krishna, Andy Zou, Rahul Gupta +6
The safety and alignment of Large Language Models (LLMs) are critical for their responsible deployment. Current evaluation methods predominantly focus on identifying and preventing…
Evaluating the Critical Risks of Amazon's Nova Premier under the Frontier Model Safety Framework
Satyapriya Krishna, Ninareh Mehrabi, Abhinav Mohanty +4
Nova Premier is Amazon's most capable multimodal foundation model and teacher for model distillation. It processes text, images, and video with a one-million-token context window,…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…