基于群体极化嵌套越狱模板的大模型安全评估技术研究OA

As large model demonstrates excellent performance in natural language processing tasks,its security issues become increasingly prominent.Jailbreak attacks bypass model security mechanisms,weaken value alignment constraints,and induce models to generate harmful content.The risks of model abuse,hijacking,and information leakage caused by such attacks pose security threats to the large language model application ecosystem.To comprehensively evaluate large model security performance,a nested jailbreak template technique based on the group polarization psychological effect is proposed,which guides models to generate complex responses through progressively nested instructions.Based on this,the NesT-HGA(nested template-hierarchical genetic algorithm)framework is constructed by integrating hierarchical genetic algorithms.Experimental results show that this method achieves an average attack success rate of over 80％across 8 mainstream large models,statistical tests confirm significant differences from existing methods,and ablation experiments verify component synergistic effects,effectively evaluating the security and robustness of large models against complex attacks.