# Secret Use of Large Language Model (LLM)

arXiv: https://arxiv.org/abs/2409.19450 · HTML: https://mlsanity.com/paper/2409.19450/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 90 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap](https://mlsanity.com/paper/2306.01941/) (2023)
- [Copyright Violations and Large Language Models](https://mlsanity.com/paper/2310.13771/) (2023)
- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (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)
- [More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering](https://mlsanity.com/paper/2311.09782/) (2023)
- [Ask the Experts: What Should Be on an IoT Privacy and Security Label?](https://mlsanity.com/paper/2002.04631/) (2020)
- [Responsible Artificial Intelligence -- from Principles to Practice](https://mlsanity.com/paper/2205.10785/) (2022)
- [Matcha: An IDE Plugin for Creating Accurate Privacy Nutrition Labels](https://mlsanity.com/paper/2402.03582/) (2024)

## Built on by (most cited first, 9 of 9)

- [Position: Privacy Is Not Just Memorization!](https://mlsanity.com/paper/2510.01645/) (2025)
- [Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective](https://mlsanity.com/paper/2506.19028/) (2025)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations](https://mlsanity.com/paper/2601.22440/) (2026)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)
- [PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration](https://mlsanity.com/paper/2512.08809/) (2025)
- [From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management](https://mlsanity.com/paper/2602.05016/) (2026)
- [How College Students Use AI to Navigate Course Readings: Evidence from an Eight-Week Study](https://mlsanity.com/paper/2602.09907/) (2026)
- ["Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work](https://mlsanity.com/paper/2603.07459/) (2026)

## Abstract (arXiv)

The advancements of Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage. Specifically, LLM users are now encouraged or required to disclose the use of LLM-generated content for varied types of real-world tasks. However, an emerging phenomenon, users' secret use of LLM, raises challenges in ensuring end users adhere to the transparency requirement. Our study used mixed-methods with an exploratory survey (125 real-world secret use cases reported) and a controlled experiment among 300 users to investigate the contexts and causes behind the secret use of LLMs. We found that such secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users. Task types were found to affect users' intentions to use secretive behavior, primarily through influencing perceived external judgment regarding LLM usage. Our results yield important insights for future work on designing interventions to encourage more transparent disclosure of the use of LLMs or other AI technologies.