May 2026: Azure Machine Learning Notebook Spoofing Vulnerability
CVE-2026-33833 Published on May 12, 2026
Azure Machine Learning Notebook Spoofing Vulnerability
Improper neutralization of special elements in output used by a downstream component ('injection') in Azure Machine Learning allows an unauthorized attacker to perform spoofing over a network.
Weakness Type
What is an Injection Vulnerability?
The software constructs all or part of a command, data structure, or record using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify how it is parsed or interpreted when it is sent to a downstream component. Software has certain assumptions about what constitutes data and control respectively. It is the lack of verification of these assumptions for user-controlled input that leads to injection problems. Injection problems encompass a wide variety of issues -- all mitigated in very different ways and usually attempted in order to alter the control flow of the process. For this reason, the most effective way to discuss these weaknesses is to note the distinct features which classify them as injection weaknesses. The most important issue to note is that all injection problems share one thing in common -- i.e., they allow for the injection of control plane data into the user-controlled data plane. This means that the execution of the process may be altered by sending code in through legitimate data channels, using no other mechanism. While buffer overflows, and many other flaws, involve the use of some further issue to gain execution, injection problems need only for the data to be parsed. The most classic instantiations of this category of weakness are SQL injection and format string vulnerabilities.
CVE-2026-33833 has been classified to as an Injection vulnerability or weakness.
Products Associated with CVE-2026-33833
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Affected Versions
Microsoft Azure Machine Learning:- Version 3.0.0 and below 1.7.6 is affected.