CVE-2025-52967 is a Server-Side Request Forgery (SSRF) vulnerability identified in the MLflow product developed by lfprojects. Specifically, the vulnerability exists in the gateway_proxy_handler component of MLflow versions prior to 3.1.0, where there is a lack of validation on the gateway_path parameter. SSRF vulnerabilities allow an attacker to induce the server to make HTTP requests to arbitrary domains or internal systems that the attacker cannot directly access. In this case, due to insufficient validation, an attacker can craft requests that cause the MLflow server to send requests to internal or external network resources on behalf of the server. The CVSS 3.1 base score is 5.8 (medium severity), with the vector indicating that the attack can be performed remotely (AV:N), requires no privileges (PR:N), and no user interaction (UI:N). The impact is limited to integrity (I:L) with no confidentiality or availability impact, and the scope is changed (S:C), meaning the vulnerability affects components beyond the initially vulnerable component. No known exploits are currently reported in the wild, and no official patches or mitigations have been linked yet. MLflow is an open-source platform widely used for managing the machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. The gateway_proxy_handler is likely used to proxy requests to other services or internal APIs, and the lack of validation on the gateway_path parameter can be abused to perform SSRF attacks, potentially allowing attackers to access internal services, bypass firewalls, or perform further attacks within the network environment hosting MLflow. Given the nature of MLflow deployments, which often reside within enterprise environments and cloud infrastructure, this vulnerability could be leveraged to pivot into internal networks or access sensitive internal APIs that are not exposed externally. However, the absence of confidentiality and availability impacts suggests that direct data leakage or denial of service is not the primary concern, but integrity impacts could include unauthorized modification or triggering of internal services or workflows.

For European organizations, the SSRF vulnerability in MLflow could lead to unauthorized internal network reconnaissance and potential manipulation of internal services, especially in environments where MLflow is integrated with sensitive machine learning workflows or data pipelines. Organizations relying on MLflow for critical AI/ML operations may face risks of integrity compromise, such as unauthorized triggering or modification of model training, deployment processes, or internal API calls. This could disrupt AI model lifecycle management or introduce malicious data or commands into workflows. While no direct data leakage or service disruption is indicated, the ability to reach internal services could facilitate lateral movement or further exploitation by attackers. Given the increasing adoption of MLflow in industries like finance, healthcare, and manufacturing across Europe, the vulnerability poses a moderate risk to operational integrity and trustworthiness of AI systems. Additionally, organizations with MLflow instances exposed to the internet or insufficiently segmented internal networks are at higher risk. The lack of required privileges or user interaction lowers the barrier for exploitation, increasing the urgency for mitigation in environments where MLflow is deployed.

1. Immediate mitigation should include restricting network access to MLflow instances, ensuring that only trusted internal users and systems can reach the MLflow server, preferably via VPN or secure internal networks. 2. Implement strict network segmentation and firewall rules to prevent MLflow servers from making arbitrary outbound requests to internal or sensitive network segments. 3. Monitor and log all proxy requests made by MLflow’s gateway_proxy_handler to detect unusual or unauthorized request patterns indicative of SSRF exploitation attempts. 4. Until an official patch is released, consider disabling or restricting the functionality of the gateway_proxy_handler if feasible, or apply custom validation on the gateway_path parameter at the application or proxy level to whitelist allowed paths and destinations. 5. Conduct thorough security reviews of MLflow deployment configurations, ensuring minimal exposure of MLflow services to the public internet and enforcing strong authentication and authorization controls. 6. Prepare to apply vendor patches promptly once available and test them in staging environments to ensure no disruption to ML workflows. 7. Educate DevOps and security teams about SSRF risks in ML platforms and incorporate SSRF detection in security monitoring tools.