MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security

• URL: http://arxiv.org/abs/2509.06807v1
• Date: Mon, 08 Sep 2025 15:39:17 GMT
• Title: MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security
• Authors: Yanrui Du, Fenglei Fan, Sendong Zhao, Jiawei Cao, Ting Liu, Bing Qin,
• Abstract summary: Large Language Models' security has emerged as a critical concern.<n>MoGU framework dynamically allocates weights by sensing hidden states.<n>MoGU_v2 exhibits strong adaptability and stable improvements across various series of LLMs.
• Score: 40.03830223238795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Large Language Models (LLMs) increasingly permeate human life, their security has emerged as a critical concern, particularly their ability to maintain harmless responses to malicious instructions. Although extensive methods have improved LLMs' security, they often lead to conservative, rejection-oriented responses that compromise practical usability. This presents a key challenge: how to advance the Pareto frontier between LLMs' usability and security, rather than necessitate a trade-off between them. To address this, we propose the MoGU framework, in which the intra-layer router dynamically allocates weights by sensing hidden states, thereby balancing the contributions of security-optimized and usability-optimized variants. Despite its initial potential, the MoGU framework faces limitations such as parameter redundancy and performance bottlenecks. To overcome these, we further propose an improved MoGU_v2 framework that establishes a tighter coupling between the routers and hidden states. In MoGU_v2, routers are embedded only in layers encoding highly classifiable security features, and backbone modules are activated during router optimization to enable bidirectional adaptation. MoGU_V2 exhibits strong adaptability and stable improvements across various series of LLMs, including mainstream LLMs serving as brains in various applications, on-device LLMs optimized for resource-constrained scenarios, and reasoning LLMs tailored for user interpretability. Meanwhile, even facing risks introduced by Instruction Fine-tuning, MoGU_v2 can easily restore security without compromising the task performance gains via a simple data-mix strategy. These comprehensive improvements highlight MoGU_V2 as a robust and versatile solution for mitigating security risks in real-world applications.

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