Combating Security and Privacy Issues in the Era of Large Language Models (2024)

Abstract: This tutorial seeks to provide a systematic summary of risks and vulnerabilities in security, privacy and copyright aspects of large language models (LLMs), and most recent solutions to address those issues. We will discuss a broad thread of studies that try to answer the following questions: (i) How do we unravel the adversarial threats that attackers may leverage in the training time of LLMs, especially those that may exist in recent paradigms of instruction tuning and RLHF processes? (ii) How do we guard the LLMs against malicious attacks in inference time, such as attacks based on backdoors and jailbreaking? (iii) How do we ensure privacy protection of user information and LLM decisions for Language Model as-a-Service (LMaaS)? (iv) How do we protect the copyright of an LLM? (v) How do we detect and prevent cases where personal or confidential information is leaked during LLM training? (vi) How should we make policies to control against improper usage of LLM-generated content? In addition, will conclude the discussions by outlining emergent challenges in security, privacy and reliability of LLMs that deserve timely investigation by the community

## introduction large language models (llms) have received wide attention from the society. these models have not only shown promising results across nlp tasks @xcite @xcite , but also emerged to be the backbone of many intelligent systems for web search @xcite , education @xcite , healthcare @xcite , e-commerce @xcite and software development @xcite . from the societal impact perspective, llms like gpt-4 and chat-gpt have shown significant potential in supporting decision making in many daily-life tasks. despite the success, the increasingly scaled sizes of llms, as well as their growing deployments in systems, services and scientific studies, are bringing along more and more emergent issues in security and privacy. on the one hand, since llms are more potent of memorizing vast amount of information, they can definitely memorize well any kind of training data that may lead to adverse behaviors, leading to backdoors @xcite @xcite that may be leveraged by adversaries to control or hack any high-stake systems that are built on top of the llms @xcite @xcite . in this context, llms may also memorize personal and confidential information that exist in corpora and the rlhf process @xcite , therefore being prone to various privacy risks including membership inference @xcite @xcite , training data extraction @xcite @xcite , and jailbreaking attacks @xcite @xcite . on the other hand, the wide usage and adaption of llms also challenge the copyright protection of models and their outputs. for example, while some models restrict commercial uses @xcite or restrict derivatives of license @xcite , it is hard to ensure that downstream developers finetuning these models will comply with the licenses. it is also hard to identify improper usage of llm generated outputs especially in scenarios like peer review @xcite and lawsuits @xcite where model generated content should be strictly controlled. moreover, while a number of llms are deployed as services @xcite , privacy protection of information in both user inputs @xcite and model decisions @xcite represents another challenge, particularly for healthcare and fintech services @xcite . this tutorial presents a comprehensive introduction of frontier research on emergent security and privacy issues in the era of llms. in particular, we try to answer the following questions: (i) how do we unravel the adversarial threats in the training time of llms, especially those that may exist in recent paradigms of instruction tuning and rlhf processes? (ii) how do we guard the llms against malicious attacks in inference time, such as attacks based on backdoors and jailbreaking? (iii) how do we addressing the privacy risks of llms, such as ensuring privacy protection of user information and llm decisions? (iv) how do we protect the copyright of an llm? (v) how do we detect and prevent cases where personal or confidential information is memorized during llm training and leaked during inference? (vi) how should we control against improper usage of llm-generated content? by addressing these critical questions, we believe it is necessary to present a timely tutorial to comprehensively summarize the new frontiers in security and privacy research in nlp, and point out the emerging challenges that deserve further attention of our community. participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use technologies, and how related technologies will realize more responsible usage of llms in end-user systems. ## outline of tutorial content this half-day tutorial presents an overview of frontier research on addressing the emergent security and privacy issues of llms. the detailed contents are outlined below. ## specification of the tutorial the proposed tutorial is considered a cutting-edge tutorial that introduces new frontiers in indirectly supervised nlp. the presented topic has not been well covered by any ⋆acl tutorials in the past 4 years. the closest one is the eacl 2023 tutorial titled "privacy-preserving natural language processing," from which our tutorial differs from several key perspectives: (i) the eacl 2023 tutorial mainly focused on privacy protection, while we cover both security and privacy issues; (ii) the eacl 2023 covers issues related to plms and earlier nlp models, while we focus on the emerging and timely issues with recent llms. ## tutorial instructors the following are biographies of the speakers. the speakers' past tutorials are listed in appx. §a.1.