在人工智能（AI）技术的迅速发展背景下，大模型的“越狱”现象成为了一个引人关注的话题。大模型“越狱”指的是通过特定技术手段绕过模型的安全机制，生成可能不符合伦理规范的输出，从而对模型的实际应用构成威胁，并对用户的安全和隐私带来潜在风险。针对这一问题，学术界和工业界共同投入研究，旨在理解和防范这些越狱行为，确保人工智能技术能够安全有效地服务于人类社会。
以大语言模型（LLMs）如GPT-4和视觉语言模型（VLMs）如CLIP和DALL-E为代表的人工智能技术，已经展现出在自然语言处理和图像与文本融合任务中的强大潜力。然而，这些技术的应用范围扩大也伴随着安全性和伦理对齐的问题。研究者发现，通过精心设计的越狱技术，攻击者能够绕过模型的内置安全机制，生成不符合伦理规范的输出。这一现象不仅威胁到模型的实际应用，还对用户的安全和隐私构成了潜在风险。
为应对这一挑战，学术界和工业界联合发布了一篇综述论文，对LLMs和VLMs的越狱现象进行了系统分类和分析。研究者将LLMs的越狱现象细分为梯度攻击、进化攻击、演示攻击、规则攻击和多代理攻击五种类型，而VLMs的越狱现象则被细分为提示到图像注入的越狱攻击、提示-图像扰动注入越狱攻击和代理模型迁移越狱攻击三种类型。这篇综述不仅提供了对各种越狱类型的详细分类和理解，还总结了现有研究中用于评测这些越狱攻击的方法，以及一些相关策略。
通过这一系统性的研究，旨在为学术界和工业界提供一个全面视角，以便更好地理解AI模型的潜在安全风险，并提出有效的应对策略。这不仅有助于提升AI技术的安全性，也促进了人工智能技术在更广泛的领域内负责任和有效地应用，为人类社会带来更大的福祉。
英语如下：
News Title: “Goliath Defense Tactics: The Frontier Tech War Against Escaping AI”
Keywords: AI Security, Escaping Defense, Large Models
News Content: Amidst the rapid development of artificial intelligence (AI) technology, the phenomenon of “escapes” by large models has become a compelling topic of discussion. An “escape” in the context of large models refers to the use of specific technical means to bypass the model’s security mechanisms, generating outputs that may violate ethical norms, thereby posing a threat to the practical application of the model and potentially compromising user security and privacy.
In response to this issue, both academia and industry have collaborated in research aimed at understanding and guarding against these escape behaviors, ensuring that AI technologies can be deployed safely and effectively for the benefit of society. Large language models (LLMs) such as GPT-4 and visual language models (VLMs) like CLIP and DALL-E exemplify the powerful potential of AI in natural language processing and image and text integration tasks. However, the expansion of their application scope also brings forth concerns over security and ethical alignment.
Researchers have identified that by employing carefully designed escape techniques, attackers can circumvent the built-in security mechanisms of these models to generate outputs that violate ethical norms. This phenomenon not only jeopardizes the practical application of the models but also poses potential risks to user security and privacy.
To tackle this challenge, a comprehensive review paper has been jointly published by academia and industry, systematically categorizing and analyzing the escape phenomena in LLMs and VLMs. The researchers have classified the escape phenomena in LLMs into five types: gradient attack, evolutionary attack, demonstration attack, rule attack, and multi-agent attack, while VLMs’ escape phenomena are further divided into three types: prompt-to-image injection escape attack, prompt-image perturbation injection escape attack, and proxy model transfer escape attack. This review not only provides a detailed classification and understanding of various escape types but also summarizes the methods used in existing research to evaluate these escape attacks, along with relevant strategies.
Through this systematic research, the aim is to provide an all-encompassing perspective for academia and industry, enabling a better understanding of the potential security risks of AI models and the formulation of effective counter-strategies. This not only enhances the security of AI technologies but also promotes their responsible and effective application across a broader range of fields, contributing to greater societal welfare.
【来源】https://www.jiqizhixin.com/articles/2024-07-29
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