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Survival Of The Safest: Towards Secure Prompt Optimization Through Interleaved Multi-objective Evolution

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities; however, the optimization of their prompts has historically prioritized performance metrics at the expense of crucial safety and security considerations. To overcome this shortcoming, we introduce "Survival of the Safest" (SoS), an innovative multi-objective prompt optimization framework that enhances both performance and secu

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