Summary of Ideator: Jailbreaking and Benchmarking Large Vision-language Models Using Themselves, by Ruofan Wang et al.
IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves
by Ruofan Wang, Juncheng Li, Yixu Wang, Bo Wang, Xiaosen Wang, Yan Teng, Yingchun Wang, Xingjun Ma, Yu-Gang Jiang
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As large Vision-Language Models (VLMs) are increasingly deployed, ensuring their safe usage has become crucial. Recent studies have focused on robustness against jailbreak attacks, which exploit model vulnerabilities to elicit harmful outputs. However, the limited availability of diverse multimodal data constrains current approaches, relying heavily on adversarial or manually crafted images derived from harmful text datasets. This paper proposes IDEATOR, a novel jailbreak method that autonomously generates malicious image-text pairs for black-box jailbreak attacks. IDEATOR leverages VLMs to create targeted jailbreak texts and pairs them with jailbreak images generated by a state-of-the-art diffusion model. The proposed method achieves high effectiveness and transferability, achieving a 94% attack success rate (ASR) in jailbreaking MiniGPT-4 and demonstrating strong transferability to other VLMs. Additionally, the paper introduces the VLBreakBench, a safety benchmark comprising multimodal jailbreak samples. Our results on 11 recently released VLMs reveal significant gaps in safety alignment, underscoring the urgent need for stronger defenses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super smart computer model that can understand and create images like humans do. But what if someone could trick this model into saying or showing something bad? That’s called a “jailbreak” attack. In this paper, researchers want to make sure these models are safe by testing how well they can withstand such attacks. They developed a new way to create fake image-text pairs that can trick the model into doing something harmful. This method works really well and can be used on many different language-image models. The researchers also created a special test set with 3,654 examples of jailbreak attacks to see how well different models perform against these attacks. They found that some models are much more vulnerable than others, which means we need to make sure we have strong defenses in place. |
Keywords
» Artificial intelligence » Alignment » Diffusion model » Transferability
