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

Summary

Researchers propose BUUAS, a Belief-updating and User-aware Security framework for scheduling prompt injection detection in LLM service deployments across edge-cloud networks. The framework combines a Bayesian-inspired belief update method with a belief-weighted contextual multi-armed bandit mechanism to prioritize high-risk requests. Experiments report that it outperforms state-of-the-art methods in detection accuracy, network throughput and resource efficiency under fluctuating user loads and varying malicious user ratios.

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