# LLM App Squatting and Cloning

arXiv: https://arxiv.org/abs/2411.07518 · HTML: https://mlsanity.com/paper/2411.07518/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 61 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 14 of 14)

- [PLeak: Prompt Leaking Attacks against Large Language Model Applications](https://mlsanity.com/paper/2405.06823/) (2024)
- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [Malla: Demystifying Real-world Large Language Model Integrated Malicious Services](https://mlsanity.com/paper/2401.03315/) (2024)
- [A First Look at GPT Apps: Landscape and Vulnerability](https://mlsanity.com/paper/2402.15105/) (2024)
- [Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations](https://mlsanity.com/paper/2111.10793/) (2021)
- [On the (In)Security of LLM App Stores](https://mlsanity.com/paper/2407.08422/) (2024)
- [LLM App Store Analysis: A Vision and Roadmap](https://mlsanity.com/paper/2404.12737/) (2024)
- [Exploring ChatGPT App Ecosystem: Distribution, Deployment and Security](https://mlsanity.com/paper/2408.14357/) (2024)
- [Opening A Pandora's Box: Things You Should Know in the Era of Custom GPTs](https://mlsanity.com/paper/2401.00905/) (2023)
- [GPT Store Mining and Analysis](https://mlsanity.com/paper/2405.10210/) (2024)
- [GPTZoo: A Large-scale Dataset of GPTs for the Research Community](https://mlsanity.com/paper/2405.15630/) (2024)
- [SpellBound: Defending Against Package Typosquatting](https://mlsanity.com/paper/2003.03471/) (2020)
- [GPT in Sheep's Clothing: The Risk of Customized GPTs](https://mlsanity.com/paper/2401.09075/) (2024)
- [Evaluating few shot and Contrastive learning Methods for Code Clone Detection](https://mlsanity.com/paper/2204.07501/) (2022)

## Built on by (most cited first, 2 of 2)

- [LaQual: An Automated Framework for LLM App Quality Evaluation](https://mlsanity.com/paper/2508.18636/) (2025)
- [Beyond Jailbreak: Unveiling Risks in LLM Applications Arising from Blurred Capability Boundaries](https://mlsanity.com/paper/2511.17874/) (2025)

## Abstract (arXiv)

Impersonation tactics, such as app squatting and app cloning, have posed longstanding challenges in mobile app stores, where malicious actors exploit the names and reputations of popular apps to deceive users. With the rapid growth of Large Language Model (LLM) stores like GPT Store and FlowGPT, these issues have similarly surfaced, threatening the integrity of the LLM app ecosystem. In this study, we present the first large-scale analysis of LLM app squatting and cloning using our custom-built tool, LLMappCrazy. LLMappCrazy covers 14 squatting generation techniques and integrates Levenshtein distance and BERT-based semantic analysis to detect cloning by analyzing app functional similarities. Using this tool, we generated variations of the top 1000 app names and found over 5,000 squatting apps in the dataset. Additionally, we observed 3,509 squatting apps and 9,575 cloning cases across six major platforms. After sampling, we find that 18.7% of the squatting apps and 4.9% of the cloning apps exhibited malicious behavior, including phishing, malware distribution, fake content dissemination, and aggressive ad injection.