Cyber Resilience

CVE-2023-35625

Medium

Published: 12 December 2023

Published
12 December 2023
Modified
21 November 2024
KEV Added
Patch
CVSS Score v3.1 4.7 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N
EPSS Score 0.0066 71.5th percentile
Risk Priority 10 60% EPSS · 20% KEV · 20% CVSS

Summary

CVE-2023-35625 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Microsoft Azure Machine Learning Software Development Kit. Its CVSS base score is 4.7 (Medium).

Operationally, ranked in the top 28.5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.

This vulnerability is AI-related — categorised as Machine Learning Libraries.

EU & UK References

Vulnerability details

Azure Machine Learning Compute Instance for SDK Users Information Disclosure Vulnerability

CWE(s)

AI Security AnalysisAI

AI Category
Machine Learning Libraries
Risk Domain
N/A
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: machine learning

Related Threats

Affected Assets

microsoft
azure machine learning software development kit
≤ 1.5.0

Mitigating Controls

Likely Mitigating Controls AI

Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.

addresses: CWE-200

Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.

addresses: CWE-200

Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.

addresses: CWE-200

Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.

addresses: CWE-200

By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.

addresses: CWE-200

Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.

addresses: CWE-200

Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.

addresses: CWE-200

Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.

addresses: CWE-200

Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.

References