Transfer Safety Awareness for Cross-Modal Safety Drift in Multimodal Large Language Models
Cross-modal safety drift lets adversaries bypass text-only safety alignment in multimodal LLMs by grounding benign queries in harmful images — a blind spot for enterprises deploying vision-language models.
Summary written by editorial AI · Source link below
arXiv:2609.02082v1 Announce Type: cross Abstract: Visual modality enhances the capabilities of multimodal large language models (MLLMs) but also introduces a safety concern: a benign textual query may convey harmful intent when grounded in a visual image. We term this cross-modal safety drift and our pilot studies show that the safety response rate for such requests is substantially lower than that for requests containing explicitly unsafe text. This paper aims to systematically study this issu
Editorial Analysis
Enterprises deploying multimodal LLMs for customer-facing or internal applications may face safety bypasses that text-only guardrails cannot catch.
Add cross-modal adversarial test cases to your MLLM red-teaming programme before production deployment.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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