Beyond Data Privacy: New Privacy Risks for Large Language Models

2025-09-16

AI Summary

This study addresses a novel privacy threat emerging during the deployment phase of large language models (LLMs): when integrated into real-world applications and endowed with autonomous decision-making capabilities, LLMs may inadvertently leak sensitive data or be exploited for large-scale privacy theft, thereby jeopardizing financial security and societal trust. Unlike prevailing research focused on training-phase privacy protection, this work pioneers systematic threat modeling for post-deployment LLMs, combining case-study analysis with evaluation of existing privacy mechanisms to identify attack surfaces overlooked by conventional data privacy paradigms. Methodologically, it conducts a rigorous characterization of deployment-specific threats, analyzes risk amplification arising from the interplay of autonomy and system integration, and proposes a runtime-oriented privacy defense framework. Key contributions include: (1) a taxonomy of deployment-phase privacy threats; (2) empirical evidence of risk escalation due to autonomy-integration coupling; and (3) a novel privacy-preserving paradigm tailored to dynamic, production-grade LLM execution environments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Examining new privacy risks from LLM deployment
• Addressing vulnerabilities enabling data leakage and attacks
• Mitigating threats to individual privacy and societal trust

Innovation

• Systematically examining emerging LLM privacy risks
• Discussing mitigation strategies for deployment vulnerabilities
• Calling for broader focus beyond data privacy