Securing Multimodal Large Language Models: Defending Against Jailbreak Attacks with Adversarial Tuning

Ziyi Yin, Yuanpu Cao, Han Liu, Ting Wang, Jinghui Chen, Fenglong Ma

Keywords: multimodal large language models, jailbreak, defense

Abstract: While multimodal large language models (MLLMs) have achieved remarkable success in recent advancements, their susceptibility to jailbreak attacks has come to light. In such attacks, adversaries exploit carefully crafted prompts to coerce models into generating harmful or undesirable content. Existing defense mechanisms often rely on external inference steps or safety alignment training, both of which are less effective and impractical when facing sophisticated adversarial perturbations in white-box scenarios. To address these challenges and bolster MLLM robustness, we introduce SafeMLLM, a novel adversarial tuning framework. SafeMLLM operates in two stages during each training iteration: (1) generating adversarial perturbations through a newly proposed contrastive embedding attack (CoE-Attack), which optimizes token embeddings under a contrastive objective, and (2) updating model parameters to neutralize the perturbation effects while preserving model utility on benign inputs. We evaluate SafeMLLM across six MLLMs and six jailbreak methods spanning multiple modalities. Experimental results show that SafeMLLM effectively defends against diverse attacks, maintaining robust performance without compromising normal interactions with users.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 7979