Summary of Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts, by Yi Liu et al.
Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts
by Yi Liu, Chengjun Cai, Xiaoli Zhang, Xingliang Yuan, Cong Wang
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multimedia (cs.MM)
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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 Large Vision Language Models (VLMs) expand the capabilities of Large Language Models (LLMs), but this advancement raises concerns about generating harmful content. While LLMs have undergone thorough security evaluations using red teaming frameworks, VLMs lack a standardized framework. To address this gap, we introduce Arondight, a red team framework specifically designed for VLMs. Arondight resolves issues related to the visual modality and diversity when transitioning existing methodologies from LLMs to VLMs. Our framework features an automated multi-modal jailbreak attack, using visual prompts generated by a red team VLM and textual prompts generated by a red team LLM guided by reinforcement learning. To enhance comprehensiveness, we integrate entropy bonuses and novelty reward metrics to incentivize the RL agent to create diverse test cases. Evaluation of ten cutting-edge VLMs exposes significant security vulnerabilities, particularly in generating toxic images and aligning multi-modal prompts. Our Arondight achieves an average attack success rate of 84.5% on GPT-4 in all fourteen prohibited scenarios defined by OpenAI for generating toxic text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that Large Vision Language Models (VLMs) are safe and don’t produce bad content. VLMs are like super powerful computers that can understand pictures as well as words. Right now, there’s no good way to test if they’re producing harmful content. To fix this, the researchers created a new tool called Arondight that helps detect problems with VLMs. They tested it on many different VLMs and found that most of them have big security issues. The paper also warns about some potentially bad responses from these models. |
Keywords
» Artificial intelligence » Gpt » Multi modal » Reinforcement learning
