Au Newcastle

To: dts-cybersecurity@newcastle.edu.au Subject: Unauthenticated AI inference endpoint, University of Newcastle, Australia (157.85.107.12)

Nicholas Michael Kloster / NuClide Research nicholas@nuclide-research.com

2026-05-01

Re: Unauthenticated Ollama AI inference endpoint, University of Newcastle, Australia IP / Host: 157.85.107.12 Severity: CRITICAL

I’m an independent security researcher. I hold CISA disclosures CVE-2025-4364 and ICSA-25-140-11 and conduct good-faith AI infrastructure research under the NuClide Research umbrella. This is an unsolicited disclosure, no engagement exists with your organization, and I have not accessed, modified, or exfiltrated any data beyond what was necessary to confirm the exposure.

Summary

University of Newcastle (Australia, Callaghan campus) server with deepseek-v4-pro:cloud cloud proxy subscription and mxbai-embed-large:latest embedding model indicating an active RAG pipeline. Raw Ollama port publicly accessible, no authentication.

Infrastructure

Models

mxbai-embed-large is a high-performance text embedding model used in RAG (retrieval-augmented generation) pipelines. Its presence alongside large language models confirms this Ollama instance is backing a document retrieval system.

Findings

F1, Unauthenticated Ollama API (CRITICAL): Port 11434 publicly accessible.
F2, DeepSeek Cloud Proxy (HIGH): deepseek-v4-pro:cloud accessible, 401 returned.
F3, RAG Pipeline Injection Surface (HIGH): Embedding model present, model injection via CVE-2025-63389 affects documents served via RAG.

Why it matters

An embedding model indicates an active RAG pipeline, documents loaded into your vector store are reachable via unauthenticated queries.

One-line fix

OLLAMA_HOST=127.0.0.1:11434
systemctl restart ollama

This rebinds Ollama to loopback only. If running in Docker: docker run -p 127.0.0.1:11434:11434 ollama/ollama.

CVE-2025-63389

All models on this instance are injectable via the unauthenticated /api/create endpoint, an attacker can overwrite any model’s system prompt or delete models entirely. No patch exists as of this disclosure.

Reference

Full technical details, parameter counts, and remediation notes are in this public research repository: AI-LLM-Infrastructure-OSINT/blob/main/case-studies/universities/AU/newcastle.md

This research is part of a broader sweep of university AI infrastructure exposures documented at: AI-LLM-Infrastructure-OSINT/blob/main/case-studies/universities/OVERVIEW.md

I’m happy to answer questions or assist with verification. No response is required.

Regards, Nicholas Michael Kloster / NuClide Research nicholas@nuclide-research.com AI-LLM-Infrastructure-OSINT