Tiny Refinements Elicit Resilience: Toward Efficient Prefix-model Against LLM Red-teaming

Liu Jiaxu, Yin Xiangyu, Wu Sihao, Wang Jianhong, Fang Meng, Yi Xinping, Huang Xiaowei. Arxiv 2024

[Paper]    
Agentic Applications Efficiency And Optimization Fine Tuning GPT Merging Model Architecture Pretraining Methods Prompting Reinforcement Learning Responsible AI Security Tools Training Techniques

With the proliferation of red-teaming strategies for Large Language Models (LLMs), the deficiency in the literature about improving the safety and robustness of LLM defense strategies is becoming increasingly pronounced. This paper introduces the LLM-based \textbf{sentinel} model as a plug-and-play prefix module designed to reconstruct the input prompt with just a few (\(<30\)) additional tokens, effectively reducing toxicity in responses from target LLMs. The sentinel model naturally overcomes the \textit{parameter inefficiency} and \textit{limited model accessibility} for fine-tuning large target models. We employ an interleaved training regimen using Proximal Policy Optimization (PPO) to optimize both red team and sentinel models dynamically, incorporating a value head-sharing mechanism inspired by the multi-agent centralized critic to manage the complex interplay between agents. Our extensive experiments across text-to-text and text-to-image demonstrate the effectiveness of our approach in mitigating toxic outputs, even when dealing with larger models like \texttt{Llama-2}, \texttt{GPT-3.5} and \texttt{Stable-Diffusion}, highlighting the potential of our framework in enhancing safety and robustness in various applications.

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