Executive Summary

This vulnerability is a Server-Side Request Forgery (SSRF) found in MLflow versions before 3.9.0. It occurs because the function that creates webhooks accepts a user-controlled URL parameter without validating it. Another function then sends HTTP POST requests to this attacker-controlled URL. Because there is no input sanitization, URL scheme filtering, or allowlist validation, an attacker who is authenticated can make the MLflow backend send requests to internal services, cloud metadata endpoints, or any external servers.

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Impact Analysis

Exploitation of this vulnerability can lead to serious impacts including theft of cloud credentials, unauthorized access to internal network services, and data exfiltration. An attacker can leverage the SSRF to make the MLflow backend communicate with internal or external systems that should not be accessible, potentially compromising sensitive information and system integrity.

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Mitigation Strategies

To mitigate this Server-Side Request Forgery (SSRF) vulnerability in MLflow versions prior to 3.9.0, you should update MLflow to version 3.9.0 or later where the webhook URL validation has been implemented.

This update includes validation of the webhook URL to prevent attackers from forcing the backend to send HTTP requests to internal or arbitrary external servers.

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Compliance Impact

The SSRF vulnerability in MLflow allows an attacker to send HTTP requests to internal services, cloud metadata endpoints, or arbitrary external servers, potentially leading to cloud credential theft, internal network access, and data exfiltration.

Such unauthorized access and data exfiltration risks could impact compliance with data protection regulations like GDPR and HIPAA, which require safeguarding sensitive data and preventing unauthorized access.

However, the provided information does not explicitly describe the direct effects on compliance with these standards.

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Detection Guidance

This vulnerability involves the MLflow backend sending HTTP POST requests to attacker-controlled URLs via the webhook functionality. Detection can focus on monitoring outgoing HTTP requests from the MLflow server to unusual or internal IP addresses or cloud metadata endpoints.

You can detect potential exploitation by inspecting logs for suspicious webhook creation or delivery activities, especially those involving unexpected URLs.

Suggested commands include using network monitoring tools or command-line utilities to capture and analyze outgoing HTTP requests from the MLflow server.

• Use tcpdump or tshark to capture outgoing HTTP POST requests from the MLflow server: tcpdump -i <interface> -A 'tcp port 80 or tcp port 443' | grep POST
• Use curl or wget to test webhook URL inputs manually if you have access to the MLflow API endpoints.
• Check MLflow server logs for webhook creation calls with unusual or external URLs.

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