CrossGuard safeguards multimodal LLMs against implicit and explicit attacks

Researchers have developed CrossGuard, a new defense system designed to protect Multimodal Large Language Models (MLLMs) from sophisticated implicit attacks. These attacks combine seemingly benign text and image inputs to convey malicious intent, making them difficult to detect. To address this, the team also created ImpForge, an automated pipeline that generates diverse implicit attack samples for training and evaluation. Experiments show CrossGuard offers superior protection against both implicit and explicit threats compared to existing defenses, while maintaining model utility. AI

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IMPACT Introduces a novel defense against implicit multimodal attacks, potentially improving MLLM security and trustworthiness.

RANK_REASON Academic paper introducing a new defense mechanism for multimodal LLMs.

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• CrossGuard
• MLLMs
• ImpForge
• Xu Zhang

• paper
• safety