Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows. This paper surveys research in the emerging interdisciplinary field of adversarial attacks on LLMs, a subfield of trustworthy ML, combining the perspectives of Natural Language Processing and Security. Prior work has shown that even safety-aligned LLMs (via instruction tuning and reinforcement learning through human feedback) can be susceptible to adversarial attacks, which exploit weaknesses and mislead AI systems, as evidenced by the prevalence of `jailbreak' attacks on models like ChatGPT and Bard. In this survey, we first provide an overview of large language models, describe their safety alignment, and categorize existing research based on various learning structures: textual-only attacks, multi-modal attacks, and additional attack methods specifically targeting complex systems, such as federated learning or multi-agent systems. We also offer comprehensive remarks on works that focus on the fundamental sources of vulnerabilities and potential defenses. To make this field more accessible to newcomers, we present a systematic review of existing works, a structured typology of adversarial attack concepts, and additional resources, including slides for presentations on related topics at the 62nd Annual Meeting of the Association for Computational Linguistics (ACL'24).

Plug and Pray: Exploiting off-the-shelf components of Multi-Modal Models

The rapid growth and increasing popularity of incorporating additional modalities (e.g., vision) into large language models (LLMs) has raised significant security concerns. This expansion of modality, akin to adding more doors to a house, unintentionally creates multiple access points for adversarial attacks. In this paper, by introducing adversarial embedding space attacks, we emphasize the vulnerabilities present in multi-modal systems that originate from incorporating off-the-shelf components like public pre-trained encoders in a plug-and-play manner into these systems. In contrast to existing work, our approach does not require access to the multi-modal system's weights or parameters but instead relies on the huge under-explored embedding space of such pre-trained encoders. Our proposed embedding space attacks involve seeking input images that reside within the dangerous or targeted regions of the extensive embedding space of these pre-trained components. These crafted adversarial images pose two major threats: 'Context Contamination' and 'Hidden Prompt Injection'-both of which can compromise multi-modal models like LLaVA and fully change the behavior of the associated language model. Our findings emphasize the need for a comprehensive examination of the underlying components, particularly pre-trained encoders, before incorporating them into systems in a plug-and-play manner to ensure robust security.