# CVE-2026-47852: Spring AI insecure ONNX model cache directory creation

Source: https://junglewise.ai/threats/cve-2026-47852-spring-ai-insecure-onnx-model-cache-directory-creation  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Published: 2026-08-27. Updated: 2026-09-25  
License: CC BY 4.0 with a link back

CVE-2026-47852 · Severity: high · CVSS 7.5 · Published 2026-08-27

Spring AI, a framework for building AI-powered applications in Java, creates a predictable cache directory for downloaded ONNX machine learning models. A local attacker on a shared host can pre-create this directory and plant a malicious model file, causing the application to load and execute untrusted code, leading to arbitrary code execution within the application context.

## Quick answers

**Is CVE-2026-47852 exploited in the wild?** No. Junglewise Threat Intelligence has no report of CVE-2026-47852 being exploited in the wild.

**How severe is CVE-2026-47852?** High severity, with a CVSS score of 7.5, published 27 August 2026.

**Which products and versions are affected?** Pivotal/VMware Spring AI 1.0.0–1.0.9, 1.1.0–1.1.8, 2.0.0.

**Is there a fix for CVE-2026-47852?** No fixed version is recorded yet. Check the vendor's advisory in the references below. If you run it, Junglewise drafts a mitigation that fits your setup and applies it once you approve, with senior security engineers behind it.

**How is CVE-2026-47852 attacked?** It is attacked with local access, in a software library.

**Am I affected by CVE-2026-47852?** Check whether anything you run is named in the advisory. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

## Technical details

This vulnerability stems from insecure use of a deterministic cache path for storing ONNX model files without proper validation or atomic creation mechanisms. A local attacker can pre-create the cache directory with a malicious ONNX model file before the Spring AI application runs. Since ONNX model deserialization can lead to code execution, loading a malicious model results in arbitrary code execution with the privileges of the application process. The attack requires local file system access on a multi-user host but no authentication or network access. Affected versions are Spring AI 1.0.0–1.0.9, 1.1.0–1.1.8, and 2.0.0.

## Affected products

- Pivotal/VMware Spring AI 1.0.0–1.0.9, 1.1.0–1.1.8, 2.0.0

## Timeline

- 2026-08-27: disclosed

## References

- [https://spring.io/security/cve-2026-47852](https://spring.io/security/cve-2026-47852)