Survival of the Safest: Towards Secure Prompt Optimization through Interleaved Multi-Objective Evolution
This work addresses the need for secure and high-performing LLM systems in industrial applications, representing an incremental advancement by integrating safety into prompt optimization.
The paper tackled the problem of optimizing prompts for large language models (LLMs) by prioritizing both performance and security, introducing the 'Survival of the Safest' (SoS) framework that uses multi-objective evolution to enhance safety and performance simultaneously, with experimental results showing notable improvements in security compared to single-objective methods.
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 security in LLMs simultaneously. SoS utilizes an interleaved multi-objective evolution strategy, integrating semantic, feedback, and crossover mutations to effectively traverse the prompt landscape. Differing from the computationally demanding Pareto front methods, SoS provides a scalable solution that expedites optimization in complex, high-dimensional discrete search spaces while keeping computational demands low. Our approach accommodates flexible weighting of objectives and generates a pool of optimized candidates, empowering users to select prompts that optimally meet their specific performance and security needs. Experimental evaluations across diverse benchmark datasets affirm SoS's efficacy in delivering high performance and notably enhancing safety and security compared to single-objective methods. This advancement marks a significant stride towards the deployment of LLM systems that are both high-performing and secure across varied industrial applications