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Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models

Yunhong He, Jianling Qiu, Wei Zhang, Zhengqing Yuan

TL;DR

The paper addresses ethical and privacy risks in transformer-based large language models by proposing a multi-layer defense that includes sensitive-input filtering, role-playing detection to prevent jailbreaking, and a rule-based content restriction framework, extended to Multi-Model LLM derivatives via TPII and TPDIT. It formalizes a threat model and demonstrates that a Total Think ensemble moderation approach can achieve state-of-the-art protection under several attack prompts while preserving core question-answering capabilities. Empirical validation across multiple open-source and proprietary models, datasets (SAP265, MMLU, GQA), and evaluation metrics (GPT-4 safety judge, VADER) shows pronounced improvements in safety with minimal or no loss in performance. The work emphasizes differentiated security levels to tailor privacy preferences, contributing to safer deployment, better data protection, and reduced social risk in AI-assisted information tasks.

Abstract

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.

Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models

TL;DR

The paper addresses ethical and privacy risks in transformer-based large language models by proposing a multi-layer defense that includes sensitive-input filtering, role-playing detection to prevent jailbreaking, and a rule-based content restriction framework, extended to Multi-Model LLM derivatives via TPII and TPDIT. It formalizes a threat model and demonstrates that a Total Think ensemble moderation approach can achieve state-of-the-art protection under several attack prompts while preserving core question-answering capabilities. Empirical validation across multiple open-source and proprietary models, datasets (SAP265, MMLU, GQA), and evaluation metrics (GPT-4 safety judge, VADER) shows pronounced improvements in safety with minimal or no loss in performance. The work emphasizes differentiated security levels to tailor privacy preferences, contributing to safer deployment, better data protection, and reduced social risk in AI-assisted information tasks.

Abstract

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.
Paper Structure (17 sections, 4 equations, 3 figures, 5 tables)

This paper contains 17 sections, 4 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Language Models
  4. Insecurity Classification
  5. Attack Methods
  6. Defensive Methods
  7. Methodology
  8. Attack Method
  9. Defense Method
  10. Experiment
  11. Experimental Setting
  12. Experimental Models
  13. Experimental Datasets
  14. Experimental Metrics
  15. Attack Results
  16. ...and 2 more sections

Figures (3)

  • Figure 1: It presents a composite of information related to Large Language Models (LLMs) with a focus on their development history, potential misuse, and a hypothetical scenario involving unethical activities. The left side outlines the timeline of LLM development from GPT-1 in 2018 to various models in 2024. The right side categorizes potential attacks on LLMs, such as character role play and text continuation, along with their impacts like social engineering and phishing emails. At the bottom, a specific unethical scenario is depicted, illustrating the use of LLMs for financial fraud.
  • Figure 2: It depicts a schematic of a defense mechanism designed for moderating content in LLMs. It illustrates a multi-step process that includes differentiating prompts, identifying common and sensitive words, and filtering sensitive content. The process outlines actions such as hiding or warning based on content analysis. Additionally, it shows a decision-making flowchart where multiple models vote to determine if a task is aggressive, leading to a consensus on whether to display the content on the screen. The diagram integrates dataset inputs, task instructions, and model collaboration to ensure a positive outcome in content moderation.
  • Figure 3: 3D bar charts depicting the performance of three language models: (a) Vicuna-13B, (b) StripedHyena-7B, and (c) Mixtral-8x7b across various content categories. The vertical axis represents the percentage scale, while the horizontal axis categorizes the types of content such as violence, suicide, religion, race, sexual, politics, and fraud. Each model's performance is assessed against different attack prompts (INSTR, IR, COG, FSH, SYN) to evaluate their robustness and content-handling capabilities.