CVE-2025-14279VulnerabilityPublished 12 Jan 2026

CVE-2025-14279: mlflow/mlflow · Origin Validation Error

Affects mlflow/mlflowSource CVE Database V5

Not known to be exploited · CVSS 8.1 High · fixed in 3.5.0 : patch mlflow/mlflow soon.

Data as of 26 Oct 2026, 07:57 UTC

CVSS 3.0 base score

8.1High

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

EPSS: 0.2% chance of exploitation in 30 days – higher than 12% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available – fixed in 3.5.0

CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N

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

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: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: Required: Successful exploitation of this vulnerability requires a user to take some action before the vulnerability can be exploited.
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: High: There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the impacted component.
Availability: None: There is no impact to availability within the impacted component.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 12 Jan 2026
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 2026-10-26.

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

Affected software

Product	Affected	Fixed in
mlflowmlflow/mlflow	Affected ≥ unspecified, < 3.5.0	Fixed in–
PyPImlflow`pkg:pypi/mlflow`Other threats	Affected <=3.4.0	Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

1. Upgrade all MLFlow installations to version 3.5.0 or later, where the vulnerability is patched. 2. Restrict access to MLFlow REST APIs to trusted internal networks only, using network segmentation and firewall rules to prevent exposure to untrusted or public networks. 3. Implement strict Content Security Policy (CSP) headers and browser security settings to limit the ability of malicious websites to perform DNS rebinding attacks. 4. Deploy Web Application Firewalls (WAFs) with DNS rebinding protections and monitor for suspicious cross-origin requests targeting MLFlow endpoints. 5. Educate users about the risks of visiting untrusted websites, especially when connected to internal MLFlow services. 6. Regularly audit MLFlow server logs for unusual API activity indicative of exploitation attempts. 7. Consider implementing additional authentication and authorization layers on MLFlow REST endpoints to reduce the risk of unauthorized access. 8. Use network-level DNS controls to prevent DNS rebinding by restricting DNS responses to known safe IP addresses.

Description

MLFlow versions up to and including 3.4.0 are vulnerable to DNS rebinding attacks due to a lack of Origin header validation in the MLFlow REST server. This vulnerability allows malicious websites to bypass Same-Origin Policy protections and execute unauthorized calls against REST endpoints. An attacker can query, update, and delete experiments via the affected endpoints, leading to potential data exfiltration, destruction, or manipulation. The issue is resolved in version 3.5.0.

Impact

For European organizations, especially those heavily invested in AI and machine learning workflows, this vulnerability poses a significant risk. Unauthorized access to MLFlow experiments can lead to exposure of sensitive intellectual property, including proprietary datasets, model parameters, and experimental results. Data manipulation or deletion could disrupt ongoing research and development, causing operational delays and financial losses. Given the collaborative nature of many European research institutions and enterprises, the risk of lateral movement or further exploitation increases if MLFlow servers are accessible from less secure networks. The confidentiality and integrity of machine learning lifecycle data are at high risk, which could also impact compliance with data protection regulations such as GDPR if personal or sensitive data is involved in experiments. The vulnerability's exploitation requires user interaction but no authentication, broadening the attack surface. Organizations with MLFlow servers exposed to the internet or accessible from employee browsers are particularly vulnerable. The absence of known exploits in the wild provides a window for proactive mitigation before active attacks emerge.

CWE-346Origin Validation Error (opens in a new tab)
Class weaknessOWASP Top 10 A07:2025: Authentication Failures (opens in a new tab)
The product does not properly verify that the source of data or communication is valid.
OWASP Cheat Sheets
OWASP Authentication (opens in a new tab)
OWASP ASVS 4.0.3
V14.5.2 (opens in a new tab)
V14.5.3 (opens in a new tab)
V1.4.4 (opens in a new tab)
Attack patterns
JSON Hijacking (aka JavaScript Hijacking) (opens in a new tab)
Cache Poisoning (opens in a new tab)
DNS Cache Poisoning (opens in a new tab)
Exploit Script-Based APIs (opens in a new tab)
Application API Message Manipulation via Man-in-the-Middle (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

Community reviews

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 12 Jan 2026, 08:38 UTC

WeaknessesCWE-346

Technical details

References

Community reviews

Related threats