CVE-2025-14279 is a vulnerability identified in MLFlow, an open-source platform widely used for managing the machine learning lifecycle. Versions up to and including 3.4.0 suffer from a lack of proper Origin header validation in the MLFlow REST server, which exposes the system to DNS rebinding attacks. DNS rebinding is a technique where an attacker manipulates DNS responses to make a victim's browser bypass the Same-Origin Policy (SOP), allowing cross-origin requests to internal or protected services. In this case, the MLFlow REST API endpoints do not verify the Origin header, enabling malicious websites to send unauthorized requests to the MLFlow server on behalf of the user. This can lead to unauthorized querying, updating, or deletion of experiments managed by MLFlow. The vulnerability does not require prior authentication but does require that a user visits a malicious website, which then exploits the DNS rebinding to interact with the MLFlow REST API. The impact includes potential data exfiltration, data integrity compromise, and destruction of valuable machine learning experiment data. The issue is resolved in MLFlow version 3.5.0 by implementing proper Origin header validation and mitigating DNS rebinding risks. No known exploits are currently reported in the wild, but the high CVSS score of 8.1 reflects the significant risk posed by this vulnerability due to its ease of exploitation and potential impact on confidentiality and integrity.

For European organizations, especially those heavily invested in AI and machine learning workflows, this vulnerability poses a significant risk. Unauthorized access to MLFlow experiments can lead to exposure of sensitive intellectual property, including proprietary datasets, model parameters, and experimental results. Data manipulation or deletion could disrupt ongoing research and development, causing operational delays and financial losses. Given the collaborative nature of many European research institutions and enterprises, the risk of lateral movement or further exploitation increases if MLFlow servers are accessible from less secure networks. The confidentiality and integrity of machine learning lifecycle data are at high risk, which could also impact compliance with data protection regulations such as GDPR if personal or sensitive data is involved in experiments. The vulnerability's exploitation requires user interaction but no authentication, broadening the attack surface. Organizations with MLFlow servers exposed to the internet or accessible from employee browsers are particularly vulnerable. The absence of known exploits in the wild provides a window for proactive mitigation before active attacks emerge.

1. Upgrade all MLFlow installations to version 3.5.0 or later, where the vulnerability is patched. 2. Restrict access to MLFlow REST APIs to trusted internal networks only, using network segmentation and firewall rules to prevent exposure to untrusted or public networks. 3. Implement strict Content Security Policy (CSP) headers and browser security settings to limit the ability of malicious websites to perform DNS rebinding attacks. 4. Deploy Web Application Firewalls (WAFs) with DNS rebinding protections and monitor for suspicious cross-origin requests targeting MLFlow endpoints. 5. Educate users about the risks of visiting untrusted websites, especially when connected to internal MLFlow services. 6. Regularly audit MLFlow server logs for unusual API activity indicative of exploitation attempts. 7. Consider implementing additional authentication and authorization layers on MLFlow REST endpoints to reduce the risk of unauthorized access. 8. Use network-level DNS controls to prevent DNS rebinding by restricting DNS responses to known safe IP addresses.