This research paper introduces a new security vulnerability, termed Feign Agent Attack (F2A), targeting Large Language Models (LLMs). The core concept revolves around exploiting the inherent trust LLMs place in integrated security detection agents.
The authors detail the three-step methodology of F2A:
Experiments conducted on various LLMs, including Mistral Large-2, Deepseek-V2.5, and GPT-4o, demonstrate a high success rate of F2A across diverse malicious prompts. The research highlights the vulnerability of LLMs to seemingly legitimate instructions, particularly in areas like fraud, antisocial behavior, and politically sensitive topics, where the line between benign and harmful content can be blurred.
The paper also proposes a defense mechanism against F2A. By prompting LLMs to critically evaluate the source and validity of security detection results, the attack's effectiveness is significantly reduced. This highlights the importance of incorporating robust verification mechanisms within LLMs to mitigate the risks of blind trust in external agents.
The research concludes by emphasizing the need for continuous improvement in LLM security measures, particularly in light of evolving attack strategies like F2A. Future research directions include refining defense mechanisms, exploring the impact of F2A on different LLM architectures, and developing comprehensive security frameworks to address the evolving landscape of LLM vulnerabilities.
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by Yupeng Ren في arxiv.org 10-14-2024
https://arxiv.org/pdf/2410.08776.pdfاستفسارات أعمق