Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms
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Abstract (arXiv)
The widespread adoption of conversational AI platforms has introduced new security and privacy risks. While these risks and their mitigation strategies have been extensively researched from a technical perspective, users' perceptions of these platforms' security and privacy remain largely unexplored. In this paper, we conduct a large-scale analysis of over 2.5M user posts from the r/ChatGPT Reddit community to understand users' security and privacy concerns and attitudes toward conversational AI platforms. Our qualitative analysis reveals that users are concerned about each stage of the data lifecycle (i.e., collection, usage, and retention). They seek mitigations for security vulnerabilities, compliance with privacy regulations, and greater transparency and control in data handling. We also find that users exhibit varied behaviors and preferences when interacting with these platforms. Some users proactively safeguard their data and adjust privacy settings, while others prioritize convenience over privacy risks, dismissing privacy concerns in favor of benefits, or feel resigned to inevitable data sharing. Through qualitative content and regression analysis, we discover that users' concerns evolve over time with the evolving AI landscape and are influenced by technological developments and major events. Based on our findings, we provide recommendations for users, platforms, enterprises, and policymakers to enhance transparency, improve data controls, and increase user trust and adoption.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2025.
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Citing papers per year: 2025: 4; 2026: 5.
9 papers.
- 2025Beyond Data Privacy: New Privacy Risks for Large Language Modelscited by 6
- 2025DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Modelscited by 4
- 2025Not My Agent, Not My Boundary? Elicitation of Personal Privacy Boundaries in AI-Delegated Information Sharingcited by 3
- 2025A Longitudinal Measurement of Privacy Policy Evolution for Large Language Modelscited by 2
- 2026"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systemscited by 0
- 2026Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agentscited by 0
- 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AIcited by 0
- 2026Culturally Situated AI Safety for Youth: Saudi Arabian Perspectives of Youth, Parents and Teacherscited by 0
- 2026Focused on the User, Overlooking the Risks: Security and Privacy Understandings, Practices and Challenges of Independent Chinese AI Agent Developerscited by 0
- 2026Focused on the User, Overlooking the Risks: Security and Privacy Understandings, Practices and Challenges of Independent Chinese AI Agent Developers2 Jun 2026
- 2026Culturally Situated AI Safety for Youth: Saudi Arabian Perspectives of Youth, Parents and Teachers29 Apr 2026
- 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI19 Apr 2026
- 2026Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agents19 Mar 2026
- 2026"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems24 Feb 2026
- 2025A Longitudinal Measurement of Privacy Policy Evolution for Large Language Models24 Nov 2025
- 2025Not My Agent, Not My Boundary? Elicitation of Personal Privacy Boundaries in AI-Delegated Information Sharing26 Sep 2025
- 2025Beyond Data Privacy: New Privacy Risks for Large Language Models16 Sep 2025
- 2025DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models11 Sep 2025