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CVE-2025-49746VulnerabilityPublished Updated

CVE-2025-49746: Microsoft Azure Machine Learning · Improper Authorization

Affects Microsoft Azure Machine LearningSource CVE Database V5

0

Not known to be exploited · CVSS 9.9 Critical.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.1 base score
9.9Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.8% chance of exploitation in 30 days – higher than 58% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H/E:U/RL:O/RC:C

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 18 Jul 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-25.

What to do

AI-suggested

To mitigate CVE-2025-49746, organizations should implement the following specific measures: 1) Monitor Microsoft security advisories closely and apply patches or updates for Azure Machine Learning as soon as they become available. 2) Enforce the principle of least privilege by reviewing and restricting Azure ML user roles and permissions to the minimum necessary for their functions. 3) Implement strong identity and access management (IAM) controls, including multi-factor authentication (MFA) for all Azure accounts with access to ML resources. 4) Use Azure Policy and role-based access control (RBAC) to limit exposure of Azure ML endpoints and resources to trusted networks and users only. 5) Continuously monitor Azure ML logs and audit trails for unusual privilege escalation attempts or anomalous activities indicative of exploitation attempts. 6) Segment Azure ML workloads and isolate critical ML environments to reduce lateral movement opportunities. 7) Educate administrators and users about the risks of privilege escalation and encourage prompt reporting of suspicious behavior. 8) Consider deploying Azure Defender for Cloud to gain enhanced threat detection and response capabilities specific to Azure services. These targeted actions go beyond generic advice by focusing on access control hardening, proactive monitoring, and rapid patch deployment tailored to Azure ML environments.

Description

Improper authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network.

References

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