VLM-Guard: Safeguarding Vision-Language Models via Fulfilling Safety Alignment Gap
Journal:
arXiv
Published Date:
Feb 14, 2025
Abstract
The emergence of vision language models (VLMs) comes with increased safety
concerns, as the incorporation of multiple modalities heightens vulnerability
to attacks. Although VLMs can be built upon LLMs that have textual safety
alignment, it is easily undermined when the vision modality is integrated. We
attribute this safety challenge to the modality gap, a separation of image and
text in the shared representation space, which blurs the distinction between
harmful and harmless queries that is evident in LLMs but weakened in VLMs. To
avoid safety decay and fulfill the safety alignment gap, we propose VLM-Guard,
an inference-time intervention strategy that leverages the LLM component of a
VLM as supervision for the safety alignment of the VLM. VLM-Guard projects the
representations of VLM into the subspace that is orthogonal to the safety
steering direction that is extracted from the safety-aligned LLM. Experimental
results on three malicious instruction settings show the effectiveness of
VLM-Guard in safeguarding VLM and fulfilling the safety alignment gap between
VLM and its LLM component.
