Overview
In the ever-evolving landscape of cybersecurity, one must never drop the guard. Today, we discuss a vulnerability that potentially impacts a wide range of enterprises utilizing Azure Machine Learning services. The vulnerability, labeled as CVE-2025-49747, is a significant security flaw that carries considerable risk due to its capability to allow an authorized attacker to elevate privileges over a network.
This vulnerability matters and warrants immediate attention as it has a CVSS (Common Vulnerability Scoring System) Severity Score of 9.9, which is significant. The high score is indicative of the potential for system compromise and data leaks, creating a substantial risk for organizations that are dependent on Azure Machine Learning for their daily operations and data analytics tasks.
Vulnerability Summary
CVE ID: CVE-2025-49747
Severity: Critical, CVSS Score 9.9
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: Potential system compromise or data leakage
Affected Products
Escape the Surveillance Era
Most apps won’t tell you the truth.
They’re part of the problem.
Phone numbers. Emails. Profiles. Logs.
It’s all fuel for surveillance.
Ameeba Chat gives you a way out.
- • No phone number
- • No email
- • No personal info
- • Anonymous aliases
- • End-to-end encrypted
Chat without a trace.
Product | Affected Versions
Azure Machine Learning | All versions prior to the security patch
How the Exploit Works
The CVE-2025-49747 exploit works by exploiting a missing authorization in Azure Machine Learning. An attacker who is already authorized on the network can abuse this vulnerability to elevate their privileges. This unauthorized privilege elevation can provide the attacker with the ability to compromise the system and potentially lead to data leakage.
Conceptual Example Code
Here is a conceptual example of how this vulnerability might be exploited. This example is a simplified representation of the attack, and actual attacks may involve more complex network interactions and malicious payloads.
POST /privilege/elevation HTTP/1.1
Host: azureml.example.com
Content-Type: application/json
Authorization: Bearer existing_auth_token
{
"elevation_payload": "malicious_code_here"
}This example shows a malicious HTTP POST request to a hypothetical privilege elevation endpoint. The malicious payload `elevation_payload` would contain the code or commands that exploit the vulnerability and elevate the attacker’s privileges.
Mitigation
The mitigation of CVE-2025-49747 involves applying the vendor patch supplied by Microsoft Azure. If unable to apply the patch immediately, using a Web Application Firewall (WAF) or Intrusion Detection System (IDS) can serve as a temporary mitigation measure. These systems can be configured to detect and block malicious network traffic that attempts to exploit this vulnerability.