提出三支柱主动防御框架，提前遏制大模型生成的虚假信息。
A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models
- 构建知识可信、推理可靠、输入鲁棒的三支柱防御体系
- 主动防御比传统方法提升63%防伪效果，但有计算开销
- 适合关注大模型安全与可信AI的研究者和开发者
大型语言模型（LLMs）在关键领域的广泛应用加剧了算法生成虚假信息的社会风险。与传统虚假内容不同，LLM生成的信息具有自强化性、高度可信且可跨语言快速传播，传统检测方法难以应对。本文提出一种主动防御范式，从被动事后检测转向前瞻式缓解策略。我们构建三支柱框架：(1) 知识可信性，强化训练与部署数据的完整性；(2) 推理可靠性，嵌入推理过程中的自我修正机制；(3) 输入鲁棒性，提升模型接口对对抗攻击的韧性。通过全面调研现有技术并进行对比元分析，结果表明主动防御策略在防止虚假信息方面较传统方法提升最高达63%，尽管存在非显著的计算开销与泛化挑战。我们认为未来研究应聚焦于协同设计稳健的知识基础、推理认证与抗攻击接口，以确保大模型在多领域有效应对虚假信息。
原文摘要 · Abstract (English)
The widespread deployment of large language models (LLMs) across critical domains has amplified the societal risks posed by algorithmically generated misinformation. Unlike traditional false content, LLM-generated misinformation can be self-reinforcing, highly plausible, and capable of rapid propagation across multiple languages, which traditional detection methods fail to mitigate effectively. This paper introduces a proactive defense paradigm, shifting from passive post hoc detection to anticipatory mitigation strategies. We propose a Three Pillars framework: (1) Knowledge Credibility, fortifying the integrity of training and deployed data; (2) Inference Reliability, embedding self-corrective mechanisms during reasoning; and (3) Input Robustness, enhancing the resilience of model interfaces against adversarial attacks. Through a comprehensive survey of existing techniques and a comparative meta-analysis, we demonstrate that proactive defense strategies offer up to 63\% improvement over conventional methods in misinformation prevention, despite non-trivial computational overhead and generalization challenges. We argue that future research should focus on co-designing robust knowledge foundations, reasoning certification, and attack-resistant interfaces to ensure LLMs can effectively counter misinformation across varied domains.
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