SALMUBench: A Benchmark for Sensitive Association-Level Multimodal Unlearning

Although Multimodal Large Language Models (MLLMs) have achieved remarkable progress across many domains, their training on large-scale multimodal datasets raises serious privacy concerns, making effective machine unlearning increasingly necessary. However, existing benchmarks mainly focus on static or short-sequence settings, offering limited support for evaluating continual privacy deletion requests in realistic deployments. To bridge this gap, we introduce ICU-Bench, a continual multimodal unlearning benchmark built on privacy-critical document data. ICU-Bench contains 1,000 privacy-sensitive profiles from two document domains, medical reports and labor contracts, with 9,500 images, 16,000 question-answer pairs, and 100 forget tasks. Additionally, new continual unlearning metrics are introduced, facilitating a comprehensive analysis of forgetting effectiveness, historical forgetting preservation, retained utility, and stability throughout the continual unlearning process. Through extensive experiments with representative unlearning methods on ICU-Bench, we show that existing methods generally struggle in continual settings and exhibit clear limitations in balancing forgetting quality, utility preservation, and scalability over long task sequences. These findings highlight the need for multimodal unlearning methods explicitly designed for continual privacy deletion.

With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data. Retraining after deletion requests or policy updates is often impractical, and targeted forgetting remains difficult because knowledge is distributed across shared representations. Multimodal unlearning addresses this challenge by enabling selective removal across modalities while retaining overall utility. This survey offers a unified, system-oriented view of multimodal unlearning across vision, language, audio, and video, grounded in recent advances, emerging applications, and open problems. Our taxonomy enables systematic comparison across model architectures and modalities, clarifying trade-offs among deletion strength, retention, efficiency, reversibility, and robustness. This survey highlights open problems and practical considerations to support future research and deployment of multimodal unlearning. We release a curated repository: https://smsnobin77.github.io/Awesome-Multimodal-Unlearning/

Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowledge while preserving model utility. Existing privacy-oriented benchmarks mainly study profile-level deletion, whereas practical requests often require finer-grained forgetting: removing a sensitive attribute while retaining other information about the same identity. We introduce attribute-level MLLM unlearning and construct AMU-Bench, covering long-text, numeric, and short-text targets, multiple forget ratios, and diverse question types. Our evaluation shows that target and retain attributes share identity-specific and visual evidence, making selective forgetting prone to residual leakage and collateral degradation. To address this challenge, we propose Causal Localization and Retain-Aware Projection (CLRP), a lightweight, training-free framework that localizes causally relevant layers and projects intermediate representations away from target-specific subspaces while preserving retain-related information. Experiments across MLLMs with different architectures and scales show that CLRP achieves a strong forgetting--retention trade-off on AMU-Bench. For example, at a 5% forget ratio, CLRP reduces Attribute Forget Set cloze accuracy from 20.00% to 4.00% while improving Attribute Retain Set accuracy from 14.77% to 17.79% in the reported LLaVA-1.5-7B and Qwen2.5-VL-3B results. These findings demonstrate effective and selective attribute-level unlearning without modifying model parameters.