{"slug": "demystifying-security-risks-of-ai-powered-applications-on-pre-trained-model-hubs", "title": "Demystifying Security Risks of AI-Powered Applications on Pre-Trained Model Hubs", "summary": "Researchers conducted the first systematic security analysis of AI-powered applications on pre-trained model hubs like Hugging Face, identifying five threat categories and ten attack vectors. Their analysis of over 970,000 public AI-Apps found thousands leaking credentials, hundreds with input injection vulnerabilities, and tens with embedded backdoors, indicating active exploitation.", "body_md": "# Computer Science > Cryptography and Security\n\n[Submitted on 29 Jun 2026]\n\n# Title:Your Space is My Zone: Demystifying the Security Risks of AI-Powered Applications on Pre-Trained Model Hubs\n\n[View PDF](/pdf/2606.30373)\n\n[HTML (experimental)](https://arxiv.org/html/2606.30373v1)\n\nAbstract:AI-powered Applications (AI-Apps), hosted on platforms such as Hugging Face, are democratizing access to pre-trained models through online inference and fine-tuning services. While lowering AI adoption barriers, these platforms introduce an unexplored attack surface, as AI-Apps are often developed by untrusted parties with weak isolation and misconfigured security settings. In this paper, we present the first systematic security analysis of AI-Apps across three leading platforms. To structure our investigation, we map the AI-App lifecycle to established risk taxonomies (e.g., OWASP), identifying five threat categories and ten attack vectors ranging from generic web flaws to high-impact architectural issues. Our analysis reveals critical failures including broken access control, insecure resource reuse, insufficient input validation, and sensitive data exposure. Notably, we uncover three novel architectural vulnerabilities inherent to platform design and demonstrate how traditional issues (e.g., world-readable logs) are uniquely amplified in this ecosystem. To assess real-world impact, we develop an analysis framework Insightor and apply it to over 970,000 public AI-Apps. Alarmingly, we find thousands of apps leaking credentials, hundreds containing input injection vulnerabilities that allow arbitrary code execution, and tens harboring embedded backdoors -- indicating active exploitation. We have responsibly disclosed all findings to the affected platforms and developers.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/demystifying-security-risks-of-ai-powered-applications-on-pre-trained-model-hubs", "canonical_source": "https://arxiv.org/abs/2606.30373", "published_at": "2026-06-30 19:10:31+00:00", "updated_at": "2026-06-30 19:20:30.626990+00:00", "lang": "en", "topics": ["ai-safety", "ai-research", "ai-infrastructure"], "entities": ["Hugging Face", "OWASP", "Insightor"], "alternates": {"html": "https://wpnews.pro/news/demystifying-security-risks-of-ai-powered-applications-on-pre-trained-model-hubs", "markdown": "https://wpnews.pro/news/demystifying-security-risks-of-ai-powered-applications-on-pre-trained-model-hubs.md", "text": "https://wpnews.pro/news/demystifying-security-risks-of-ai-powered-applications-on-pre-trained-model-hubs.txt", "jsonld": "https://wpnews.pro/news/demystifying-security-risks-of-ai-powered-applications-on-pre-trained-model-hubs.jsonld"}}