SALMUBench: A Benchmark for Sensitive Association-Level Multimodal Unlearning

As multimodal models like CLIP become integral to downstream systems, the need to remove sensitive information is critical. However, machine unlearning for contrastively-trained encoders remains underexplored, and existing evaluations fail to diagnose fine-grained, association-level forgetting. We introduce SALMUBench (Sensitive Association-Level Multimodal Unlearning), a benchmark built upon a synthetic dataset of 60K persona-attribute associations and two foundational models: a \textit{Compromised} model polluted with this data, and a \textit{Clean} model without it. Both are trained from scratch on a 400M-pair \texttt{retain} set to isolate unlearning effects. We propose a novel evaluation protocol with structured holdout sets (\texttt{holdout_identity}, \texttt{holdout_association}) to precisely measure unlearning efficacy and collateral damage. Our benchmark reveals that while utility-efficient deletion is feasible, current methods exhibit distinct failure modes: they either fail to forget effectively or over-generalize by erasing more than intended. SALMUBench sets a new standard for comprehensive unlearning evaluation, and we publicly release our dataset, models, evaluation scripts, and leaderboards to foster future research.