  Existing frameworks for assessing robustness of large language models (LLMs)
overly depend on specific benchmarks, increasing costs and failing to evaluate
performance of LLMs in professional domains due to dataset limitations. This
paper proposes a framework that systematically evaluates the robustness of LLMs
under adversarial attack scenarios by leveraging knowledge graphs (KGs). Our
framework generates original prompts from the triplets of knowledge graphs and
creates adversarial prompts by poisoning, assessing the robustness of LLMs
through the results of these adversarial attacks. We systematically evaluate
the effectiveness of this framework and its modules. Experiments show that
adversarial robustness of the ChatGPT family ranks as GPT-4-turbo > GPT-4o >
GPT-3.5-turbo, and the robustness of large language models is influenced by the
professional domains in which they operate.
