LLM部署带来新隐私风险，需警惕数据泄露与系统滥用
Beyond Data Privacy: New Privacy Risks for Large Language Models
- 分析大模型部署阶段的新隐私威胁机制
- 揭示模型自主能力被恶意利用的攻击路径
- 适合关注AI安全与合规的研究者和开发者
大型语言模型（LLMs）在自然语言理解、推理和自主决策方面取得显著进展，但其发展也伴随重大隐私问题。尽管研究多聚焦于训练阶段的数据隐私风险，却较少关注部署阶段的新威胁。随着LLM广泛集成于各类应用，并被利用其自主能力进行攻击，导致系统出现无意数据泄露和恶意数据外泄。攻击者可借助这些系统发起大规模隐私攻击，危及个人隐私、金融安全与社会信任。本文系统分析了这些新兴隐私风险，探讨潜在缓解策略，并呼吁研究界拓展视野，超越传统数据隐私范畴，构建应对强大LLM及其系统的新型防御体系。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have achieved remarkable progress in natural language understanding, reasoning, and autonomous decision-making. However, these advancements have also come with significant privacy concerns. While significant research has focused on mitigating the data privacy risks of LLMs during various stages of model training, less attention has been paid to new threats emerging from their deployment. The integration of LLMs into widely used applications and the weaponization of their autonomous abilities have created new privacy vulnerabilities. These vulnerabilities provide opportunities for both inadvertent data leakage and malicious exfiltration from LLM-powered systems. Additionally, adversaries can exploit these systems to launch sophisticated, large-scale privacy attacks, threatening not only individual privacy but also financial security and societal trust. In this paper, we systematically examine these emerging privacy risks of LLMs. We also discuss potential mitigation strategies and call for the research community to broaden its focus beyond data privacy risks, developing new defenses to address the evolving threats posed by increasingly powerful LLMs and LLM-powered systems.
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