A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models

TL;DR

This survey reviews proactive defense strategies for large language models to combat misinformation, emphasizing a shift from detection to anticipatory mitigation, and highlights their effectiveness and challenges.

Contribution

It introduces a Three Pillars framework for proactive defense and provides a comprehensive survey and meta-analysis of existing techniques in this area.

Findings

Proactive strategies improve misinformation prevention by up to 63%.

They face challenges like computational overhead and generalization issues.

The paper outlines future directions for robust LLM defenses.

Abstract

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.