逆势而上：在多语言嵌入反演攻击中克服类型学、文字和语言混淆问题
Against All Odds: Overcoming Typology, Script, and Language Confusion in Multilingual Embedding Inversion Attacks
AI总结：
本研究针对多语言LLM的嵌入反演攻击，跨20种语言、8个语系和12种文字进行实验，发现阿拉伯文字、西里尔文字及印度-雅利安语系语言尤为脆弱，并揭示语言混淆瓶颈的可预测模式。
摘要：
大型语言模型（LLMs）容易受到网络攻击者的恶意影响，这些攻击包括对抗性攻击、后门攻击和嵌入反演攻击。为此，新兴的LLM安全领域旨在研究并防御此类威胁。迄今为止，该领域的大部分工作集中在单语英语模型上，然而，新兴研究表明，多语言LLM可能比单语模型更容易受到各种攻击。虽然已有研究调查了少数欧洲语言子集的嵌入反演，但将这些发现外推到来自不同语系和不同文字的语言具有挑战性。为此，我们在嵌入反演攻击的背景下探索多语言LLM的安全性，并调查了跨越8个以上语系和12种文字的20种语言的跨语言和跨文字反演。我们的研究结果表明，使用阿拉伯文字和西里尔文字书写的语言，以及印度-雅利安语系的语言，特别容易受到嵌入反演攻击。我们进一步观察到，反演模型往往遭受语言混淆，有时会大大降低攻击的有效性。因此，我们系统地探索了反演模型的这一瓶颈，发现了攻击者可以利用的可预测模式。最终，本研究旨在加深该领域对多语言LLM面临的突出安全漏洞的理解，并提高对最易受这些攻击负面影响的语言的认识。
英文摘要：
Large Language Models (LLMs) are susceptible to malicious influence by cyber attackers through intrusions such as adversarial, backdoor, and embedding inversion attacks. In response, the burgeoning field of LLM Security aims to study and defend against such threats. Thus far, the majority of works in this area have focused on monolingual English models, however, emerging research suggests that multilingual LLMs may be more vulnerable to various attacks than their monolingual counterparts. While previous work has investigated embedding inversion over a small subset of European languages, it is challenging to extrapolate these findings to languages from different linguistic families and with differing scripts. To this end, we explore the security of multilingual LLMs in the context of embedding inversion attacks and investigate cross-lingual and cross-script inversion across 20 languages, spanning over 8 language families and 12 scripts. Our findings indicate that languages written in Arabic script and Cyrillic script are particularly vulnerable to embedding inversion, as are languages within the Indo-Aryan language family. We further observe that inversion models tend to suffer from language confusion, sometimes greatly reducing the efficacy of an attack. Accordingly, we systematically explore this bottleneck for inversion models, uncovering predictable patterns which could be leveraged by attackers. Ultimately, this study aims to further the field's understanding of the outstanding security vulnerabilities facing multilingual LLMs and raise awareness for the languages most at risk of negative impact from these attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
