CVE-2025-49746: Improper Authorization in Azure Machine Learning Leading to Privilege Escalation

Overview

The Common Vulnerabilities and Exposures (CVE) system has recently identified a critical security vulnerability, identified as CVE-2025-49746. This vulnerability exists in Azure Machine Learning, a popular cloud-based machine learning service used by many businesses and organizations worldwide. Alarmingly, this vulnerability could allow an attacker to elevate their privileges within a system, potentially enabling unauthorized access to sensitive information or system resources.
The severity of this issue is underscored by its high CVSS Severity Score of 9.9, indicating that if exploited, it could have severe implications, including system compromise or data leakage. With its widespread usage, Azure Machine Learning users are strongly urged to understand this vulnerability and take the appropriate mitigation steps.

Vulnerability Summary

CVE ID: CVE-2025-49746
Severity: Critical (9.9)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: System Compromise, Data Leakage

Affected Products

Product | Affected Versions

Azure Machine Learning | All versions before patch

How the Exploit Works

The vulnerability arises due to improper authorization mechanisms in Azure Machine Learning. An attacker, after gaining initial access to the network, can exploit this flaw to elevate their privileges. By leveraging this increased access, they can then execute commands or access resources that would otherwise be restricted, leading to a potential system compromise or data leakage.

Conceptual Example Code

Here is a conceptual example that illustrates how this vulnerability might be exploited:

POST /api/v1/execute-command HTTP/1.1
Host: azureml.example.com
Authorization: Bearer {low_privilege_token}
Content-Type: application/json
{
"command": "cat /etc/shadow",
"elevate": true
}

In this example, the attacker uses a low privilege token they have access to, in order to execute a command that would normally require higher privileges. The `”elevate”: true` part of the payload is where the improper authorization flaw is exploited, as the system fails to properly check the user’s privileges before executing the command.

Recommendations for Mitigation

Users of Azure Machine Learning are strongly advised to apply the latest vendor patch to mitigate this vulnerability. In cases where immediate patching is not possible, implementing a Web Application Firewall (WAF) or Intrusion Detection System (IDS) can provide temporary mitigation. However, these should not be viewed as a long-term solution, but rather as additional layers of security. The only comprehensive solution to this vulnerability is to apply the vendor’s patch as soon as possible.