Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing
  Security in Large Language Models
Yunhong He Jianling Qiu Wei Zhang Zhengqing Yuan
Recent advancements in large language models (LLMs) have significantly
enhanced capabilities in natural language processing and artificial
intelligence. These models, including GPT-3.5 and LLaMA-2, have revolutionized
text generation, translation, and question-answering tasks due to the
transformative Transformer model. Despite their widespread use, LLMs present
challenges such as ethical dilemmas when models are compelled to respond
inappropriately, susceptibility to phishing attacks, and privacy violations.
This paper addresses these challenges by introducing a multi-pronged approach
that includes: 1) filtering sensitive vocabulary from user input to prevent
unethical responses; 2) detecting role-playing to halt interactions that could
lead to 'prison break' scenarios; 3) implementing custom rule engines to
restrict the generation of prohibited content; and 4) extending these
methodologies to various LLM derivatives like Multi-Model Large Language Models
(MLLMs). Our approach not only fortifies models against unethical manipulations
and privacy breaches but also maintains their high performance across tasks. We
demonstrate state-of-the-art performance under various attack prompts, without
compromising the model's core functionalities. Furthermore, the introduction of
differentiated security levels empowers users to control their personal data
disclosure. Our methods contribute to reducing social risks and conflicts
arising from technological abuse, enhance data protection, and promote social
equity. Collectively, this research provides a framework for balancing the
efficiency of question-answering systems with user privacy and ethical
standards, ensuring a safer user experience and fostering trust in AI
technology.