Efficient Prompt Security Detection for LLM Service Deployment in Edge-Cloud Networks

Large Language Models are vulnerable to prompt injection attacks (tricking an AI by hiding malicious instructions in its input), which poses security risks during deployment. This paper proposes BUUAS, a framework that uses Bayesian-inspired belief updates and a belief-weighted contextual multi-armed bandit mechanism (a decision-making approach that learns which security checks to prioritize) to detect prompt injection attacks more efficiently by focusing on high-risk user requests in edge-cloud networks (systems that process data both locally on edge devices and in remote data centers).