CyberSecBulletin
CVE-2026-33833NVD status: Analyzed

Azure Machine Learning: Improper Neutralization of Special Elements in Output Used by a Downstream Component

Published Updated By the CyberSecBulletin desk

CVE-2026-33833 is a high-severity improper neutralization of special elements in output used by a downstream component vulnerability in Azure Machine Learning from Microsoft, with a CVSS 3.1 base score of 8.2 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, but only if a user takes part, for example by opening a link or file. The record lists version 1.7.6 as the first without the flaw, so updating Azure Machine Learning to that release or later removes the exposure. There is no confirmed exploitation so far. EPSS estimates a 0.5% chance of exploitation within 30 days, which is low compared with other CVEs.

Next patch cycle

High severity with no evidence of exploitation so far.

What to do

  1. Update Azure Machine Learning to 1.7.6 or a later version.
  2. Read the advisory on msrc.microsoft.com for workarounds and exact fixed builds.
  3. Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
  4. Because the attack needs a user to take part, warn users about unexpected links and files related to this product until it is patched.

Affected products and versions

VendorProductAffected versionsFixed in
MicrosoftAzure Machine Learning3.0.0 up to, but not including, 1.7.61.7.6

Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.

How the severity score breaks down

Attack vector
NetworkCan be exploited remotely over a network, including the internet.
Attack complexity
LowNo special conditions are needed; the attack works reliably.
Privileges required
NoneNo account or login is needed.
User interaction
RequiredA user must take an action such as opening a file or link.
Scope
ChangedThe attack can reach beyond the vulnerable component into other systems.
Confidentiality
HighAll data handled by the component can be read.
Integrity
LowLimited modification of data is possible.
Availability
NoneAvailability is not affected.

Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:L/A:N. Scored by CNA (microsoft.com).

Official description

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.

Source: the CVE record as published by its numbering authority, via NVD and CVE.org.

The underlying weakness: Improper Neutralization of Special Elements in Output Used by a Downstream Component

The product 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.

How developers prevent this class of flaw

  • Programming languages and supporting technologies might be chosen which are not subject to these issues.
  • Utilize an appropriate mix of allowlist and denylist parsing to filter control-plane syntax from all input.

More Improper Neutralization of Special Elements in Output Used by a Downstream Component vulnerabilities (CWE-74)

Timeline

  1. CVE record published.
  2. CISA published its SSVC assessment (exploitation: none, automatable: no, technical impact: partial).
  3. Record last changed materially (score, affected products, status or references).

References

Other vulnerabilities in Azure Machine Learning

All Azure Machine Learning vulnerabilities

Recent Improper Neutralization of Special Elements in Output Used by a Downstream Component vulnerabilities

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