CVE-2025-47995VulnerabilityPublished 18 Jul 2025Updated 26 Feb 2026

CVE-2025-47995: Microsoft Azure Machine Learning · Weak Authentication

Affects Microsoft Azure Machine LearningSource CVE Database V5

Not known to be exploited · CVSS 6.5 Medium.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.1 base score

6.5Medium

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.7% chance of exploitation in 30 days – higher than 55% 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:U/C:H/I:N/A:N/E:U/RL:O/RC:C

Base metrics

Attack Vector: Network
Attack Complexity: Low
Privileges Required: Low
User Interaction: None
Scope: Unchanged
Confidentiality: High
Integrity: None
Availability: None

Temporal metrics

Exploit Code Maturity: Unproven
Remediation Level: Official Fix
Report Confidence: Confirmed

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: Low: The attacker is authorized with (i.e., requires) privileges that provide basic user capabilities that could normally affect only settings and files owned by a user. Alternatively, an attacker with Low privileges may have the ability to cause an impact only to non-sensitive resources.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: High: There is total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: None: There is no impact to availability within the impacted component.
Exploit Code Maturity: Unproven: No exploit code is available, or an exploit is theoretical.
Remediation Level: Official Fix: A complete vendor solution is available. Either the vendor has issued an official patch, or an upgrade is available.
Report Confidence: Confirmed: Detailed reports exist, or functional reproduction is possible (functional exploits may provide this). Source code is available to independently verify the assertions of the research, or the author or vendor of the affected code has confirmed the presence of the vulnerability.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 18 Jul 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

AI-suggested

Organizations should implement the following specific mitigations: 1) Review and strengthen authentication configurations within Azure Machine Learning, including enforcing multi-factor authentication (MFA) for all users with any level of privileges. 2) Limit the assignment of privileges to the minimum necessary, applying the principle of least privilege to reduce the potential impact of privilege escalation. 3) Monitor Azure Machine Learning environments for unusual privilege escalation attempts or anomalous access patterns using Azure Security Center and Azure Sentinel. 4) Apply network segmentation and restrict network access to Azure Machine Learning services to trusted IP ranges and VPNs to reduce exposure. 5) Stay informed on Microsoft advisories for patches or updates addressing this vulnerability and apply them promptly once available. 6) Conduct regular security assessments and penetration tests focusing on authentication mechanisms in cloud AI services. 7) Educate administrators and users about the risks of weak authentication and the importance of secure credential management. These steps go beyond generic advice by focusing on Azure-specific controls and proactive monitoring tailored to this vulnerability.

Description

Weak authentication in Azure Machine Learning allows an authorized attacker to elevate privileges over a network.

Impact

The primary impact of this vulnerability is unauthorized privilege escalation within Azure Machine Learning environments, leading to potential exposure of sensitive data, intellectual property, or proprietary AI models. Confidentiality is at high risk, as attackers could access data beyond their authorized scope. Although integrity and availability are not directly affected, the breach of confidentiality could lead to secondary impacts such as data leakage or compliance violations. Organizations worldwide that rely on Azure Machine Learning for AI development, especially those handling sensitive or regulated data (e.g., healthcare, finance, government), face increased risk of data breaches and intellectual property theft. The network-based nature of the attack means that attackers with some level of access to the environment could exploit this vulnerability remotely, increasing the threat surface. The lack of known exploits currently limits immediate widespread impact but also means organizations should proactively address the issue before exploitation becomes common.

CWE-1390Weak Authentication (opens in a new tab)
Class weaknessOWASP Top 10 A07:2025: Authentication Failures (opens in a new tab)
The product uses an authentication mechanism to restrict access to specific users or identities, but the mechanism does not sufficiently prove that the claimed identity is correct.
OWASP Cheat Sheets
OWASP Authentication (opens in a new tab)
OWASP ASVS 4.0.3
V2.8.5 (opens in a new tab)
V2.8.4 (opens in a new tab)
V9.2.3 (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

msrc.microsoft.com/update-guide/vulnerability/CVE-2025-47995 (opens in a new tab)

Community reviews

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Exploitation evidence

What to do

Description

AI analysisMachine-generated, updated 27 Feb 2026, 02:48 UTC

WeaknessesCWE-1390

Technical details

References

Community reviews

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