JailDAM introduces a novel detection framework to identify malicious attempts to bypass safety mechanisms in multimodal large language models.

• Addresses the critical security risk of jailbreak attacks in vision-language models
• Uses adaptive memory components to accurately identify manipulation attempts
• Provides a robust defense system for identifying harmful content requests
• Helps ensure safer deployment of multimodal AI systems

This research is vital for organizations implementing vision-language models, as it significantly enhances security measures against sophisticated attacks that could otherwise lead to harmful content generation and reputational damage.

JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model