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Learning To See But Forgetting To Follow: Visual Instruction Tuning Makes LLMs More Prone To Jailbreak Attacks
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Augmenting Large Language Models (LLMs) with image-understanding capabilities has resulted in a boom of high-performing Vision-Language models (VLMs). While studying the alignment of LLMs to human values has received widespread attention, the safety of VLMs has not received the same attention. In this paper, we explore the impact of jailbreaking on three state-of-the-art VLMs, each using a distinct modeling approach. By comparing each VLM to their respective LLM backbone, we find that each VLM is more susceptible to jailbreaking. We consider this as an undesirable outcome from visual instruction-tuning, which imposes a forgetting effect on an LLM's safety guardrails. Therefore, we provide recommendations for future work based on evaluation strategies that aim to highlight the weaknesses of a VLM, as well as take safety measures into account during visual instruction tuning.
Forward citations
Cited by 4 Pith papers
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$PC^2$: Politically Controversial Content Generation via Jailbreaking Attacks on GPT-based Text-to-Image Models
PC2, a multilingual descriptive-rewriting attack, makes GPT-based text-to-image models generate politically controversial images of real public figures despite safety filters.
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VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models
VERA-V learns a distribution of text-image jailbreak prompts via variational inference, achieving higher attack success rates and lower toxicity detection than prior multimodal red-teaming methods.
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Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness
Adversarial training at both the CLIP pre-training stage and the LLaVA instruction-tuning stage produces vision-language models with state-of-the-art robustness and near-baseline clean performance.
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A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations
A survey of LVLM safety that adds a lifecycle taxonomy and new benchmark results showing Janus-Pro-7B has weaker safety than several open-source LVLMs.
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