Protect Your Prompts: Protocols for IP Protection in LLM Applications
Not on the map. Why?
Abstract (arXiv)
With the rapid adoption of AI in the form of large language models (LLMs), the potential value of carefully engineered prompts has become significant. However, to realize this potential, prompts should be tradable on an open market. Since prompts are, at present, generally economically non-excludable, by virtue of their nature as text, no general competitive market has yet been established. This note discusses two protocols intended to provide protection of prompts, elevating their status as intellectual property, thus confirming the intellectual property rights of prompt engineers, and potentially supporting the flourishing of an open market for LLM prompts.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 9 Jun 2023.
Builds on
6 of 10 references.
References · 6
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,586
- 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 817
- 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 616
- 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
- 2023A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutionscited by 35
- 2023Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineeringcited by 4
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2023Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competitioncited by 68
- 2025Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systemscited by 1
- 2025User Negotiations of Authenticity, Ownership, and Governance on AI-Generated Video Platforms: Evidence from Soracited by 0
- 2026ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embeddingcited by 0
- 2026ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding20 Apr 2026
- 2025Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems29 Dec 2025
- 2025User Negotiations of Authenticity, Ownership, and Governance on AI-Generated Video Platforms: Evidence from Sora5 Dec 2025
- 2023Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition24 Oct 2023