Tests whether filtering pretraining data can safeguard open-weight protein models against dual-use biological capabilities; finds it selective and effective but not durable against fine-tuning.
Proposes a certification framework giving high-confidence upper bounds on residual concept leakage after unlearning in text-to-image diffusion models, finding attack-based evaluations underestimate leakage by 16.2%.
Presents SafeTune, a library unifying methods for detecting and repairing safety drift in fine-tuned LLMs, including weight recovery, safety-constrained fine-tuning, unlearning and steering, with evaluation utilities and case studies.
Shows that abliterating refusal directions from open-weight models causes off-target shifts in optimism and confidence on a decision task, so 'uncensored' models behave as measurably different decision-makers.