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TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis

Authors: Xiaorui Wu, Xiaofeng Mao, Fei Li, Xin Zhang, Xuanhong Li, Chong Teng, Donghong Ji, Zhuang Li

Published: 2025-05-30

arXiv ID: 2505.24672v1

Added to Library: 2025-06-02 03:00 UTC

Red Teaming

📄 Abstract

Large Language Models (LLMs) excel in various natural language processing tasks but remain vulnerable to generating harmful content or being exploited for malicious purposes. Although safety alignment datasets have been introduced to mitigate such risks through supervised fine-tuning (SFT), these datasets often lack comprehensive risk coverage. Most existing datasets focus primarily on lexical diversity while neglecting other critical dimensions. To address this limitation, we propose a novel analysis framework to systematically measure the risk coverage of alignment datasets across three essential dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics. We further introduce TRIDENT, an automated pipeline that leverages persona-based, zero-shot LLM generation to produce diverse and comprehensive instructions spanning these dimensions. Each harmful instruction is paired with an ethically aligned response, resulting in two datasets: TRIDENT-Core, comprising 26,311 examples, and TRIDENT-Edge, with 18,773 examples. Fine-tuning Llama 3.1-8B on TRIDENT-Edge demonstrates substantial improvements, achieving an average 14.29% reduction in Harm Score, and a 20% decrease in Attack Success Rate compared to the best-performing baseline model fine-tuned on the WildBreak dataset.

🔍 Key Points

  • Introduction of TRIDENT, a framework designed for enhancing LLM safety by generating diversified red-teaming datasets across three dimensions: Lexical Diversity, Malicious Intent Diversity, and Jailbreak Tactic Diversity.
  • Development of two comprehensive datasets, TRIDENT-Core and TRIDENT-Edge, which provide diverse instructions and ethically aligned responses, with TRIDENT-Core containing 26,311 examples and TRIDENT-Edge including 18,773 examples focused on jailbreak techniques.
  • Demonstration of substantial safety improvements through fine-tuning the Meta-Llama-3.1-8B model on the TRIDENT-Edge dataset, leading to a reported 14.29% reduction in Harm Score and a 20% decrease in Attack Success Rate compared to baseline models.
  • Implementation of an automated persona-based data generation pipeline which minimizes human intervention, thereby enhancing scalability and reducing biases in safety alignment datasets.
  • Conducting thorough evaluations and ablation studies that validate the effectiveness of each diversity dimension in contributing to the overall safety and reliability of LLMs.

💡 Why This Paper Matters

The paper presents a significant advancement in the field of AI safety, addressing critical gaps in current LLM alignment techniques. The implementation of TRIDENT and its multi-dimensional approach to dataset generation not only enhances the robustness of LLMs against potential dangers but also establishes a framework that can be adapted as new threats emerge. Therefore, it is highly relevant in the context of ongoing efforts to create safer AI systems.

🎯 Why It's Interesting for AI Security Researchers

This paper is of critical interest to AI security researchers because it systematically addresses the vulnerabilities present in large language models and proposes concrete methodologies to strengthen their alignment with ethical norms. The introduction of a diverse and automated red-teaming dataset generation approach allows researchers to better understand and combat malicious uses of AI, making it an essential contribution within the field of responsible AI development.

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