This research introduces adaptive guardrails for large language models that dynamically adjust content filtering based on user trust levels.

• Implements a sociotechnical approach to LLM safety, balancing protection with access rights
• Utilizes trust modeling to customize guardrail strictness for different user groups
• Enhances security through in-context learning mechanisms that adapt to interactions
• Delivers a flexible framework for managing sensitive content exposure in AI systems

This advancement is critical for security professionals as it enables fine-grained control over LLM outputs without implementing rigid, one-size-fits-all restrictions that hamper legitimate use cases.

Trust-Oriented Adaptive Guardrails for Large Language Models