White-box Multimodal Jailbreaks Against Large Vision-Language Models
Ruofan Wang, Xingjun Ma, Hanxu Zhou, Chuanjun Ji, Guangnan Ye, Yu-Gang Jiang·May 28, 2024
Summary
This paper investigates the vulnerability of large vision-language models (VLMs) to multimodal attacks, specifically focusing on the Universal Master Key (UMK) strategy. UMK, a white-box attack, combines an adversarial image prefix and text suffix to generate toxic responses and bypass alignment defenses. The study, using MiniGPT-4 as a target, shows a 96% success rate in jailbreaking, indicating the need for improved alignment techniques to mitigate the risk of harmful content generation. UMK outperforms unimodal methods and highlights the expanded attack surface in multimodal models, necessitating defense strategies that address both text and image modalities. Researchers also explore related topics like detoxification, robustness, and bias mitigation in these models to enhance their security and performance.
Introduction
Background
[ ] Emergence of large VLMs and their impact on AI applications
[ ] Importance of multimodal alignment in preventing harmful content
Objective
[ ] To assess the threat posed by UMK attacks on VLMs
[ ] Investigate the success rate and effectiveness of UMK in jailbreaking
[ ] Highlight the need for improved defense strategies
Method
Data Collection
[ ] Target model: MiniGPT-4 as a case study
[ ] Adversarial data generation using UMK strategy
Data Preprocessing
[ ] Cleaning and preprocessing of adversarial and original data
[ ] Evaluation dataset preparation for jailbreaking success rate
UMK Strategy Analysis
[ ] Adversarial image prefix and text suffix generation
[ ] Comparison with unimodal attack methods
Evaluation Metrics
[ ] Jailbreaking success rate
[ ] Toxic response generation rate
Robustness and Detoxification
Detoxification Techniques
[ ] Identifying and mitigating toxic content generation
[ ] Effectiveness of detoxification methods on UMK attacks
Model Robustness
[ ] Assessing model resilience to adversarial inputs
[ ] Strategies for improving robustness against UMK
Bias Mitigation
[ ] Analysis of bias in VLMs and its connection to UMK vulnerability
[ ] Implementing bias mitigation techniques to enhance security
Defense Strategies
Unimodal vs. Multimodal Defenses
[ ] The importance of joint defense for both text and image modalities
[ ] Proposals for multimodal defense mechanisms
Conclusion
[ ] Summary of findings and implications for VLM security
[ ] Recommendations for future research and model development
Future Directions
[ ] Open challenges and opportunities in mitigating multimodal attacks
[ ] Potential directions for enhancing alignment and security in VLMs
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What strategy does the Universal Master Key (UMK) employ to attack vision-language models?
Why do the findings of this paper emphasize the need for improved defense techniques in multimodal models?
What is the primary focus of the paper mentioned by the user?
What is the success rate of the UMK attack on MiniGPT-4, as mentioned in the study?