Demystifying Security Risks of AI-Powered Applications on Pre-Trained Model Hubs
Researchers present the first systematic security analysis of AI-Apps on platforms like Hugging Face, identifying five threat categories and ten attack vectors. Over 970,000 public AI-Apps were analyzed, revealing thousands with leaked credentials, hundreds vulnerable to code execution, and tens with embedded backdoors.
-->
[Submitted on 29 Jun 2026]
Title:Your Space is My Zone: Demystifying the Security Risks of AI-Powered Applications on Pre-Trained Model Hubs
View a PDF of the paper titled Your Space is My Zone: Demystifying the Security Risks of AI-Powered Applications on Pre-Trained Model Hubs, by Yacong Gu and 8 other authors
View PDF HTML (experimental)
Abstract: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.
Comments: 18 pages, accepted by CCS 2026
Subjects:
Cryptography and Security (cs.CR)
Cite as: arXiv:2606.30373 [cs.CR]
(or arXiv:2606.30373v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.30373
arXiv-issued DOI via DataCite
Submission history
From: Yacong Gu [view email] [v1] Mon, 29 Jun 2026 14:34:43 UTC (329 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Your Space is My Zone: Demystifying the Security Risks of AI-Powered Applications on Pre-Trained Model Hubs, by Yacong Gu and 8 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CR
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both 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.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)