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CVE-2025-49747VulnerabilityPublished Updated

CVE-2025-49747: Microsoft Azure Machine Learning · Missing Authorization

Affects Microsoft Azure Machine LearningSource CVE Database V5

0

Not known to be exploited · CVSS 9.9 Critical.

Data as of 26 Oct 2026, 06:26 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 56% 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 2026-10-26.

What to do

AI-suggested

To mitigate CVE-2025-49747, organizations should implement the following specific measures: 1) Monitor Azure security advisories closely and apply patches or updates from Microsoft immediately upon release. 2) Enforce the principle of least privilege rigorously within Azure Machine Learning environments, ensuring users and service principals have only necessary permissions. 3) Utilize Azure Role-Based Access Control (RBAC) to tightly control access to machine learning resources and audit role assignments regularly. 4) Enable and review detailed logging and monitoring of Azure Machine Learning activities to detect anomalous privilege escalation attempts. 5) Implement network segmentation and conditional access policies to restrict access to Azure Machine Learning services from trusted networks and devices only. 6) Conduct regular security assessments and penetration testing focused on authorization controls within cloud AI services. 7) Educate administrators and developers on secure configuration practices for Azure Machine Learning to prevent misconfigurations that could exacerbate the vulnerability. 8) Consider deploying additional runtime security controls or anomaly detection tools that can identify suspicious behavior indicative of privilege escalation. These targeted actions go beyond generic advice by focusing on Azure-specific controls and proactive monitoring tailored to the nature of this vulnerability.

Description

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

References

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