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

• URL: http://arxiv.org/abs/2507.05288v1
• Date: Sat, 05 Jul 2025 09:52:21 GMT
• Title: A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models
• Authors: Shuliang Liu, Hongyi Liu, Aiwei Liu, Bingchen Duan, Qi Zheng, Yibo Yan, He Geng, Peijie Jiang, Jia Liu, Xuming Hu,
• Abstract summary: This paper introduces a proactive defense paradigm, shifting from passive post hoc detection to anticipatory mitigation strategies.<n>We demonstrate that proactive defense strategies offer up to 63% improvement over conventional methods in misinformation prevention.
• Score: 23.046017613121737
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• On the Robustness of Verbal Confidence of LLMs in Adversarial Attacks [23.95254828487318]
  We present the first comprehensive study on the robustness of verbal confidence under adversarial attacks.<n>We introduce a novel framework for attacking verbal confidence scores through both perturbation and jailbreak-based methods.<n>Our findings underscore the urgent need to design more robust mechanisms for confidence expression in large language models.
  arXiv  Detail & Related papers  (2025-07-09T02:19:46Z)
• A Survey on Model Extraction Attacks and Defenses for Large Language Models [55.60375624503877]
  Model extraction attacks pose significant security threats to deployed language models.<n>This survey provides a comprehensive taxonomy of extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks.<n>We examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios.
  arXiv  Detail & Related papers  (2025-06-26T22:02:01Z)
• Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies [0.0]
  This paper conducts a comparison of approaches to detecting misinformation between text-based, multimodal, and agentic approaches.<n>We evaluate the effectiveness of fine-tuned models, zero-shot learning, and systematic fact-checking mechanisms in detecting misinformation across different topic domains.
  arXiv  Detail & Related papers  (2025-03-02T04:31:42Z)
• Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging [12.644923600594176]
  Adversarial attacks pose significant threats to the reliability and safety of deep learning models.<n>This paper introduces a novel framework that integrates conformal prediction with game-theoretic defensive strategies.
  arXiv  Detail & Related papers  (2024-11-07T02:20:04Z)
• Robust Image Classification: Defensive Strategies against FGSM and PGD Adversarial Attacks [0.0]
  Adversarial attacks pose significant threats to the robustness of deep learning models in image classification. This paper explores and refines defense mechanisms against these attacks to enhance the resilience of neural networks.
  arXiv  Detail & Related papers  (2024-08-20T02:00:02Z)
• Imposter.AI: Adversarial Attacks with Hidden Intentions towards Aligned Large Language Models [13.225041704917905]
  This study unveils an attack mechanism that capitalizes on human conversation strategies to extract harmful information from large language models. Unlike conventional methods that target explicit malicious responses, our approach delves deeper into the nature of the information provided in responses.
  arXiv  Detail & Related papers  (2024-07-22T06:04:29Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• Data Poisoning for In-context Learning [49.77204165250528]
  In-context learning (ICL) has been recognized for its innovative ability to adapt to new tasks.<n>This paper delves into the critical issue of ICL's susceptibility to data poisoning attacks.<n>We introduce ICLPoison, a specialized attacking framework conceived to exploit the learning mechanisms of ICL.
  arXiv  Detail & Related papers  (2024-02-03T14:20:20Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension [96.62963688510035]
  Reading comprehension models often overfit to nuances of training datasets and fail at adversarial evaluation. We present several effective adversaries and automated data augmentation policy search methods with the goal of making reading comprehension models more robust to adversarial evaluation.
  arXiv  Detail & Related papers  (2020-04-13T17:20:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.