Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation

• URL: http://arxiv.org/abs/2412.13666v1
• Date: Wed, 18 Dec 2024 09:48:53 GMT
• Title: Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation
• Authors: Aneta Zugecova, Dominik Macko, Ivan Srba, Robert Moro, Jakub Kopal, Katarina Marcincinova, Matus Mesarcik,
• Abstract summary: Large language models (LLMs) can be effectively misused for generating disinformation news articles. This study fills this gap by evaluation of vulnerabilities of recent open and closed LLMs. Our results demonstrate the need for stronger safety-filters and disclaimers.
• Score: 0.5070610131852027
• License:
• Abstract: The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts rises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles following predefined narratives. Their capabilities to generate personalized (in various aspects) content have also been evaluated and mostly found usable. However, a combination of personalization and disinformation abilities of LLMs has not been comprehensively studied yet. Such a dangerous combination should trigger integrated safety filters of the LLMs, if there are some. This study fills this gap by evaluation of vulnerabilities of recent open and closed LLMs, and their willingness to generate personalized disinformation news articles in English. We further explore whether the LLMs can reliably meta-evaluate the personalization quality and whether the personalization affects the generated-texts detectability. Our results demonstrate the need for stronger safety-filters and disclaimers, as those are not properly functioning in most of the evaluated LLMs. Additionally, our study revealed that the personalization actually reduces the safety-filter activations; thus effectively functioning as a jailbreak. Such behavior must be urgently addressed by LLM developers and service providers.

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