MDIT -Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models (2025)

Abstract: The widespread use of Large Multimodal Models (LMMs) has raised concerns about model toxicity. However, current research mainly focuses on explicit toxicity, with less attention to some more implicit toxicity regarding prejudice and discrimination. To address this limitation, we introduce a subtler type of toxicity named dual-implicit toxicity and a novel toxicity benchmark termed MDIT-Bench: Multimodal Dual-Implicit Toxicity Benchmark. Specifically, we first create the MDIT-Dataset with dual-implicit toxicity using the proposed Multi-stage Human-in-loop In-context Generation method. Based on this dataset, we construct the MDIT-Bench, a benchmark for evaluating the sensitivity of models to dual-implicit toxicity, with 317,638 questions covering 12 categories, 23 subcategories, and 780 topics. MDIT-Bench includes three difficulty levels, and we propose a metric to measure the toxicity gap exhibited by the model across them. In the experiment, we conducted MDIT-Bench on 13 prominent LMMs, and the results show that these LMMs cannot handle dual-implicit toxicity effectively. The model’s performance drops significantly in hard level, revealing that these LMMs still contain a significant amount of hidden but activatable toxicity. The data will be released upon the paper’s acceptance.

## introduction large multimodal models (lmms) have made great progress in recent years. however, with the widespread usage of lmms @xcite @xcite , the emergence of model toxicity has become a serious problem. model toxicity refers to the offensive, abusive, hateful content that the model may generate in its output, such as racism, sexism, and homophobia @xcite @xcite . the presence of model toxicity can cause serious consequences. many previous works were dedicated to solving explicit toxicity, as shown in figure 1 @xcite . this type of toxicity contains discriminatory and insulting language, which is easily identifiable and simple to detox @xcite . some works also addressed implicit toxicity. unlike explicit toxicity, implicit toxicity does not include directly offensive language. instead, it expresses toxicity through euphemistic means such as metaphors and sarcasm @xcite , as shown in figure 1(b) . however, previous works have not addressed a subtler form of toxicity regarding prejudice, which we term dual-implicit toxicity (figure 1(c) ). it cannot be detected solely through a single visual or textual modality. instead, it requires synthesizing information from both modalities to be identified. to address the research gap related to dualimplicit toxicity, we introduce the multimodal dual-implicit toxic (mdit) dataset, constructed using the proposed @xcite in-context generation method. this method generates diverse data and incorporates human intervention to align with human values. the mdit-dataset contains 112,873 toxic questions. based on established definitions and categorizations of toxic content @xcite @xcite , we classify the dataset into 12 categories and 23 subcategories. next, we introduce the mdit-bench, a benchmark designed to assess the sensitivity of lmms to dual-implicit toxicity regarding fine-grained prejudice and discrimination. it comprises 317,638 test questions across three difficulty levels: easy, medium, and hard. the easy level contains 91,892 questions without dual-implicit toxicity, while both the medium and hard levels include 112,873 questions each, sourced from the mdit-dataset. we review the rationality and validity of this benchmark through human evaluation. 13 lmms are evaluated using mdit-bench, and the results indicate limited sensitivity to dual-implicit toxicity, highlighting the need for further improvement. the hard level builds on the medium level by adding long-context jailbreaking. we propose the hidden toxicity metric (ht) to quantify the increased toxicity exhibited by the model at the hard level compared to the medium level, referred to as "hidden toxicity". results show that the tested lmms exhibit significant hidden toxicity, with many models achieving about half the accuracy at the hard level compared to the medium level. in summary, our contributions are as follows: ## method model toxicity is a critical issue. motivated by the lack of focus on more subtle forms of toxicity in existing works, we introduce the concept of dualimplicit toxicity. we then propose the multimodal dual-implicit toxic (mdit) dataset and construct the mdit-bench, designed to assess the sensitivity of lmms to dual-implicit toxicity. to ensure the validity of the mdit-bench, we conduct human evaluation. additionally, we propose a metric for quantifying hidden toxicity in hard level. ## discussion during the experiment, we found that using euphemistic expressions and incorporating images can lead the model to generate different outputs for the same viewpoint. for example, the statement "people who live in the city are socially inept" is often refuted by most models. however, if the statement is phrased more gently as "is life busier and relationships more fickle in a place like the one in the picture?" along with an image of a city, the model often fails to provide an appropriate response. we believe the challenge with dual-implicit toxicity is the need to integrate multi-modal information from the image and text while understanding the deep, hidden meanings in the semantics. current models struggle with synchronizing these tasks, and we hope our work provides direction for developing safer models. ## conclusion we introduce the concept of dual-implicit toxicity, a more subtle form of toxicity than conventional implicit toxicity, which necessitates the integration of multiple modalities for effective detection. to evaluate the sensitivity of large multimodal models (lmms) to dual-implicit toxicity, we present mdit-bench, a multimodal dual-implicit toxicity benchmark constructed from mdit dataset using the multi-stage human-in-loop in-context generation method. mdit-bench comprises 317,638 questions across 12 categories and 23 subcategories, covering 780 topics. mdit-bench includes three difficulty levels, and we propose a metric to measure the toxicity gap exhibited by the model across them. we conduct mdit-benchmarking on 13 lmms, with the results indicating a need for improvement in these models' ability to detect and resist dual-implicit toxicity. additionally, the results at the hard level reveal that most lmms exhibit concerning levels of hidden toxicity. these findings highlight the need for greater attention to dual-implicit toxicity to enhance the safety, reliability, and overall effectiveness of lmms.