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CVE-2026-33833: Azure Machine Learning Spoofing Vulnerability

CVE-2026-33833 describes an injection vulnerability in Azure Machine Learning that allows an unauthorized attacker to perform spoofing over a network.

CVE-2026-33833 is an injection vulnerability affecting Azure Machine Learning. According to the Microsoft advisory published on May 12, 2026, improper neutralization of special elements in output used by a downstream component allows an attacker to perform spoofing over a network. The vulnerability has a CVSS v3.1 score of 8.2, indicating a high severity. Successful exploitation of this vulnerability could allow an attacker to mislead users or systems that rely on Azure Machine Learning output.

Attack Chain

  1. Attacker identifies a vulnerable Azure Machine Learning endpoint.
  2. Attacker crafts a malicious input containing special elements (e.g., shell metacharacters or HTML/JavaScript code).
  3. The malicious input is submitted to Azure Machine Learning.
  4. Azure Machine Learning processes the input without proper neutralization of special elements.
  5. The un-neutralized input is used as output by a downstream component.
  6. The downstream component interprets the special elements as commands or code.
  7. The attacker is able to spoof the output.

Impact

Successful exploitation of CVE-2026-33833 could allow an attacker to perform spoofing attacks, potentially leading to the dissemination of false information, the redirection of users to malicious websites, or the compromise of systems that rely on Azure Machine Learning output. The impact could range from minor annoyance to significant reputational damage or financial loss, depending on the context in which Azure Machine Learning is used.

Recommendation

  • Apply the security update provided by Microsoft to patch CVE-2026-33833 in Azure Machine Learning (reference: https://msrc.microsoft.com/update-guide/vulnerability/CVE-2026-33833).
  • Deploy the Sigma rule Detect Suspicious Output in Azure Machine Learning to identify potential exploitation attempts based on unusual output characteristics.
  • Implement input validation and output encoding measures to prevent injection vulnerabilities in Azure Machine Learning and other applications that process user-supplied data.

Detection coverage 2

Detect Suspicious Output in Azure Machine Learning

medium

Detects CVE-2026-33833 exploitation — identifies potentially malicious output from Azure Machine Learning that contains common injection payloads.

sigma tactics: initial_access techniques: T1190 sources: webserver

Detect URI Parameter Injection Attempts

medium

Detects CVE-2026-33833 exploitation — identifies potentially malicious URI parameters that may be indicative of an injection vulnerability.

sigma tactics: initial_access techniques: T1190 sources: webserver

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