Is Your Multimodal Language Model Oversensitive to Safe Queries?
Xirui Li Hengguang Zhou Ruochen Wang Tianyi Zhou Minhao Cheng Cho-Jui Hsieh
Humans are prone to cognitive distortions — biased thinking patterns that lead to exaggerated responses to specific stimuli, albeit in very different contexts.
This paper demonstrates that advanced Multimodal Large Language Models (MLLMs) exhibit similar tendencies.
While these models are designed to respond queries under safety mechanism, they sometimes reject harmless queries in the presence of certain visual stimuli, disregarding the benign nature of their contexts.
As the initial step in investigating this behavior, we identify three representative types of stimuli that trigger the oversensitivity of existing MLLMs: $\textbf{\textit{Exaggerated Risk}}$, $\textbf{\textit{Negated Harm}}$, and $\textbf{\textit{Counterintuitive Interpretation}}$.
To systematically evaluate MLLMs' oversensitivity to these stimuli, we propose the $\textbf{M}$ultimodal $\textbf{O}$ver$\textbf{S}$en$\textbf{S}$itivity $\textbf{Bench}$mark (MOSSBench).
This toolkit consists of 300 manually collected benign multimodal queries, cross-verified by third-party reviewers (AMT).
Empirical studies using MOSSBench on 20 MLLMs reveal several insights:
(1). Oversensitivity is prevalent among SOTA MLLMs, with refusal rates reaching up to $\textbf{76}$\% for harmless queries.
(2). Safer models are more oversensitive: increasing safety may inadvertently raise caution and conservatism in the model’s responses.
(3). Different types of stimuli tend to cause errors at specific stages — perception, intent reasoning, and safety judgement — in the response process of MLLMs.
These findings highlight the need for refined safety mechanisms that balance caution with contextually appropriate responses, improving the reliability of MLLMs in real-world applications.
Safety, Adversarial Robustness, Multimodal Large Language Models, Alignment
This work studies the issue of oversensitivity in multimodal large language models (MLLMs) by introducing the first dedicated benchmark, MOSSBench, designed to systematically evaluate and understand oversensitivity in these models.