The rapid evolution of multimodal foundation models has led to significant advancements in cross-modal understanding and generation across diverse modalities, including text, images, audio, and video. However, these models remain susceptible to jailbreak attacks, which can bypass built-in safety mechanisms and induce the production of potentially harmful content. Consequently, understanding the methods of jailbreak attacks and existing defense mechanisms is essential to ensure the safe deployment of multimodal generative models in real-world scenarios, particularly in security-sensitive applications. To provide comprehensive insight into this topic, this survey reviews jailbreak and defense in multimodal generative models. First, given the generalized lifecycle of multimodal jailbreak, we systematically explore attacks and corresponding defense strategies across four levels: input, encoder, generator, and output. Based on this analysis, we present a detailed taxonomy of attack methods, defense mechanisms, and evaluation frameworks specific to multimodal generative models. Additionally, we cover a wide range of input-output configurations, including modalities such as Any-to-Text, Any-to-Vision, and Any-to-Any within generative systems. Finally, we highlight current research challenges and propose potential directions for future research. The open-source repository corresponding to this work can be found at https://github.com/liuxuannan/Awesome-Multimodal-Jailbreak.

Current multimodal misinformation detection (MMD) methods often assume a single source and type of forgery for each sample, which is insufficient for real-world scenarios where multiple forgery sources coexist. The lack of a benchmark for mixed-source misinformation has hindered progress in this field. To address this, we introduce MMFakeBench, the first comprehensive benchmark for mixed-source MMD. MMFakeBench includes 3 critical sources: textual veracity distortion, visual veracity distortion, and cross-modal consistency distortion, along with 12 sub-categories of misinformation forgery types. We further conduct an extensive evaluation of 6 prevalent detection methods and 15 large vision-language models (LVLMs) on MMFakeBench under a zero-shot setting. The results indicate that current methods struggle under this challenging and realistic mixed-source MMD setting. Additionally, we propose an innovative unified framework, which integrates rationales, actions, and tool-use capabilities of LVLM agents, significantly enhancing accuracy and generalization. We believe this study will catalyze future research into more realistic mixed-source multimodal misinformation and provide a fair evaluation of misinformation detection methods.

The massive generation of multimodal fake news exhibits substantial distribution discrepancies, prompting the need for generalized detectors. However, the insulated nature of training within specific domains restricts the capability of classical detectors to obtain open-world facts. In this paper, we propose FakeNewsGPT4, a novel framework that augments Large Vision-Language Models (LVLMs) with forgery-specific knowledge for manipulation reasoning while inheriting extensive world knowledge as complementary. Knowledge augmentation in FakeNewsGPT4 involves acquiring two types of forgery-specific knowledge, i.e., semantic correlation and artifact trace, and merging them into LVLMs. Specifically, we design a multi-level cross-modal reasoning module that establishes interactions across modalities for extracting semantic correlations. Concurrently, a dual-branch fine-grained verification module is presented to comprehend localized details to encode artifact traces. The generated knowledge is translated into refined embeddings compatible with LVLMs. We also incorporate candidate answer heuristics and soft prompts to enhance input informativeness. Extensive experiments on the public benchmark demonstrate that FakeNewsGPT4 achieves superior cross-domain performance compared to previous methods. Code will be available.