A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models (2025)

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.

## introduction the exponential adoption of large language models (llms) across mission-critical domains-from healthcare diagnostics to legal analysis-has paradoxically amplified their societal risks through algorithmic generation of sophisticated misinformation. unlike traditional false content manually crafted by bad actors, llm-generated misinformation inherits dangerous emergent properties: self-reinforcing plausibility through coherent reasoning chains, exponential propagation velocity via api-driven automation, and cross-lingual contamination exceeding human moderation capacity. recent acl findings reveal that conventional posthoc detection methods exhibit 38.7% false negative rates against llm-generated misinformation @xcite , exposing a critical paradigm mismatch: existing defenses remain reactionary in an era demanding proactive defense. this survey establishes a paradigm shift from passive detection to proactive defense through the three pillars of preventative assurance: (1) knowledge credibility-fortifying factual grounding from training data to post-deployment editing; (2) inference reliability-embedding selfcorrective mechanisms across decoding and alignment processes; (3) input robustness-hardening interaction surfaces against adversarial manipulations. unlike modular safety components, our framework conceptualizes misinformation defense as a continuum spanning knowledge internalization, reasoning certification, and input sanitization. the urgency manifests in three dimensions of escalating risk: -knowledge decay: llms exhibit 22.1% quarterly accuracy erosion on timesensitive facts without systematic updates @xcite -hallucination cascades: multi-turn dialogues incur 39.8% error amplification in medical qa systems @xcite -adversarial exploitation: gradient-based jailbreaks achieve 79% success rates against commercial safeguards @xcite our analysis reveals that state-of-the-art proactive strategies demonstrate 42-63% superiority over conventional detection in misinformation prevention @xcite , albeit with non-trivial tradeoffs in computational overhead (1.5-3× latency) and generalization gaps (18-25% cross-domain variance). the path forward demands co-design of rigorous knowledge foundations, probabilisti-cally certified reasoning, and attack-resistant interfaces-a systems challenge rivaling llm creation itself. this survey makes three pioneering contributions: 1. taxonomy of proactive defense mechanism mapping 127 techniques across knowledge, inference, and input safeguards 2. rigorous comparative evaluation revealing efficacy-latencyrobustness tradeoffs through meta-analysis of 48 benchmark studies 3. safety-defense co-design framework unifying knowledge editing, selfalignment, and adversarial hardening into proactive assurance lifecycle the arms race against misinformation generation requires nothing less than rebuilding llms as self-vaccinating systems-where every knowledge retrieval, reasoning step, and user interaction embodies intrinsic defenses against falsehoods. this survey charts the path from theoretical possibility to engineering reality. ## preliminary before delving into detailed defense strategies, it is essential to contextualize the phenomena of misinformation and outline the framework of proactive defense. ## knowledge credibility as a fundamental mechanism for large language models (llms) to combat misinformation, research on knowledge credibility revolves around two complementary paradigms: internal knowledge (static facts encoded within model parameters) and external knowledge (dynamically retrieved and verified evidence). recent advancements demonstrate that optimizing internal knowledge-through enhanced data quality ( §3.1.1) and parameter-level knowledge editing ( §3.1.2)-significantly strengthens the intrinsic credibility of models. however, inherent limitations persist in static parametric knowledge, evidenced by temporal decay in synthetic data and logical conflicts arising from editing operations. these challenges drive the synergistic evolution of retrieval-augmented architectures ( §3.2.1), establishing a dual-layer defense framework of "internal consolidation and external verification." this chapter systematically examines the technical landscape of the field, dissecting comprehensive trust assurance mechanisms spanning from foundational data construction to dynamic knowledge integra- knowledge credibility unverified external knowledge unverified internal knowledge inference reliability input robustness raw prompt optimized prompt adversarial prompt verified internal knowledge verified external knowledge knowledge editing rag response leveraging v.s. outdated knowledge v.s. updated knowledge v.s. inaccurate knowledge v.s. factual knowledge curation raw dataset trustful dataset note filtering factual alignment reliable decoding adversarial training tion. ## inference reliability ensuring trustworthy outputs during generation requires multi-layered safeguards that govern the reasoning process itself. decoding strategies-examined in section 4.1-form the operational core of inference reliability by mediating token selection through contrastive mechanisms and entropy-aware optimization. these foundational techniques establish the first checkpoint against misinformation propagation, working synergistically with subsequent factual alignment protocols (section 4.2) and adversarial hardening measures (section 4.3). together, they create a defensein-depth architecture where each generation step undergoes contextual grounding, logical verification, and robustness filtering-transforming raw probability distributions into verifiable knowledge streams. ## input robustness ensuring input robustness requires synergistic strategies to harden llms against adversarial manipulation. this chapter analyzes proactive prompt engineering ( §5.1)-leveraging structured instructions to guide factual precision-and reactive defenses against injection attacks ( §5.2). while hierarchical verification frameworks and self-regulatory mechanisms demonstrate promise, persistent challenges in computational efficiency and adaptive threat mitigation underscore the need for unified defense paradigms. the interplay between instruction design and adversarial pattern neutralization forms the cornerstone of evolving safeguards in dynamic threat environments. ## future work and conclusion looking ahead, future research should concentrate on three key directions. first, the development of dynamic knowledge integration mechanisms is essential to mitigate knowledge decay and enhance the adaptability of language models across diverse domains. second, the implementation of advanced inference techniques, including probabilistic reasoning and self-verification strategies, can significantly reduce hallucinations and improve factual accuracy. third, efforts should be directed toward enhancing input robustness through methods such as adversarial training and prompt optimization, thereby minimizing susceptibility to adversarial manipulation. these directions aim to develop selfvaccinating llms-systems inherently resistant to misinformation and capable of maintaining high factual integrity. addressing the ongoing arms race against misinformation requires a systems-level approach to model design, uniting data integrity, reasoning robustness, and adversarial resilience. this survey presented a proactive defense strategy framework against misinformation in llms, shifting from post-hoc detection to prevention. by focusing on the three pillars of preventative assurance-knowledge credibility, inference reliability, and input robustness-we highlighted significant advancements and stressed the importance of co-designing systems that integrate these strategies.