SEA: Low-Resource Safety Alignment for Multimodal Large Language Models via Synthetic Embeddings
This addresses safety risks in MLLMs for AI security applications, offering a low-resource solution that is incremental over existing alignment methods.
The paper tackles the problem of safety vulnerabilities in Multimodal Large Language Models (MLLMs) by proposing SEA, a method that uses synthetic embeddings to enable multimodal safety alignment with only textual data, achieving significant security improvements and synthesizing embeddings in 24 seconds on a single GPU.
Multimodal Large Language Models (MLLMs) have serious security vulnerabilities.While safety alignment using multimodal datasets consisting of text and data of additional modalities can effectively enhance MLLM's security, it is costly to construct these datasets. Existing low-resource security alignment methods, including textual alignment, have been found to struggle with the security risks posed by additional modalities. To address this, we propose Synthetic Embedding augmented safety Alignment (SEA), which optimizes embeddings of additional modality through gradient updates to expand textual datasets. This enables multimodal safety alignment training even when only textual data is available. Extensive experiments on image, video, and audio-based MLLMs demonstrate that SEA can synthesize a high-quality embedding on a single RTX3090 GPU within 24 seconds. SEA significantly improves the security of MLLMs when faced with threats from additional modalities. To assess the security risks introduced by video and audio, we also introduced a new benchmark called VA-SafetyBench. High attack success rates across multiple MLLMs validate its challenge. Our code and data will be available at https://github.com/ZeroNLP/SEA.