BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks
Yunhan Zhao, Xiang Zheng, Lin Luo, Yige Li, Xingjun Ma, Yu-Gang Jiang

TL;DR
BlueSuffix is a novel reinforcement learning-based defense method that enhances the robustness of vision-language models against jailbreak attacks without degrading performance on benign inputs.
Contribution
It introduces a comprehensive blue-team defense framework with visual and textual purifiers and a reinforcement-tuned suffix generator, addressing limitations of existing unimodal and bimodal defenses.
Findings
Outperforms baseline defenses on four VLMs and four safety benchmarks.
Effectively defends against jailbreak attacks without performance degradation.
Demonstrates robustness across multiple models and attack scenarios.
Abstract
In this paper, we focus on black-box defense for VLMs against jailbreak attacks. Existing black-box defense methods are either unimodal or bimodal. Unimodal methods enhance either the vision or language module of the VLM, while bimodal methods robustify the model through text-image representation realignment. However, these methods suffer from two limitations: 1) they fail to fully exploit the cross-modal information, or 2) they degrade the model performance on benign inputs. To address these limitations, we propose a novel blue-team method BlueSuffix that defends target VLMs against jailbreak attacks without compromising its performance under black-box setting. BlueSuffix includes three key components: 1) a visual purifier against jailbreak images, 2) a textual purifier against jailbreak texts, and 3) a blue-team suffix generator using reinforcement fine-tuning for enhancing…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Fire Detection and Safety Systems · Hand Gesture Recognition Systems
MethodsFocus
