# Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI

arXiv: https://arxiv.org/abs/2604.17270 · HTML: https://mlsanity.com/paper/2604.17270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 95 references that are in the map.
- First posted on arXiv 19 Apr 2026.

## Builds on (most cited first, 22 of 22)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [Counterfactual Memorization in Neural Language Models](https://mlsanity.com/paper/2112.12938/) (2021)
- [Machine Unlearning: Solutions and Challenges](https://mlsanity.com/paper/2308.07061/) (2023)
- ["It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents](https://mlsanity.com/paper/2309.11653/) (2023)
- [Typology of Risks of Generative Text-to-Image Models](https://mlsanity.com/paper/2307.05543/) (2023)
- [Machine Unlearning: A Comprehensive Survey](https://mlsanity.com/paper/2405.07406/) (2024)
- [A Review on Machine Unlearning](https://mlsanity.com/paper/2411.11315/) (2024)
- [Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks](https://mlsanity.com/paper/2310.07879/) (2023)
- [Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data](https://mlsanity.com/paper/2406.13843/) (2024)
- [Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM](https://mlsanity.com/paper/2409.07871/) (2024)
- [Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Health](https://mlsanity.com/paper/2507.10695/) (2025)
- [Opening A Pandora's Box: Things You Should Know in the Era of Custom GPTs](https://mlsanity.com/paper/2401.00905/) (2023)
- [Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms](https://mlsanity.com/paper/2504.06552/) (2025)
- [Ask the Experts: What Should Be on an IoT Privacy and Security Label?](https://mlsanity.com/paper/2002.04631/) (2020)
- [Users' Mental Models of Generative AI Chatbot Ecosystems](https://mlsanity.com/paper/2501.19211/) (2025)
- [Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory](https://mlsanity.com/paper/2508.07664/) (2025)
- [Helping Johnny Make Sense of Privacy Policies with LLMs](https://mlsanity.com/paper/2501.16033/) (2025)
- [Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts](https://mlsanity.com/paper/2509.23525/) (2025)
- [SoK (or SoLK?): On the Quantitative Study of Sociodemographic Factors and Computer Security Behaviors](https://mlsanity.com/paper/2404.10187/) (2024)

## Abstract (arXiv)

Users increasingly rely on consumer-facing generative AI (GenAI) for tasks ranging from everyday needs to sensitive use cases. Yet, it remains unclear whether and how existing security and privacy (S&P) communications in GenAI tools shape users' adoption decisions and experiences. Understanding how users seek, interpret, and evaluate S&P information is critical for designing usable transparency that users can trust and act on. We conducted semi-structured interviews and design sessions with 21 U.S. GenAI users. Our findings suggest that available S&P information rarely drove initial adoption in practice, as participants often perceived it as incomplete, ineffective, or not credible. Instead, they relied on rough proxies (e.g., popularity) to infer S&P practices. After adoption, S&P uncertainty constrained participants' willingness to use GenAI tools, especially for high-stakes purposes, and, in some cases, contributed to discontinued use. Participants therefore called for transparency that supports decisions and actions through trustworthy information (e.g., independent evaluations) and usable interfaces (e.g., on-demand disclosure). We categorize participants' desired design practices into five dimensions to facilitate systematic future investigation into best practices. We conclude with recommendations for researchers, designers, and policymakers to improve S&P transparency in consumer-facing GenAI.