VulnerabilityPublished 23 Jun 2025

CVE-2025-52967: lfprojects MLflow · Server-Side Request Forgery (SSRF)

Affects lfprojects MLflowSource CVE Database V5

Not known to be exploited · CVSS 5.8 Medium : upgrade to 3.1.0 when convenient.

Data as of 26 Oct 2026, 15:59 UTC

CVSS 3.1 base score

5.8Medium

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

EPSS: 0.4% chance of exploitation in 30 days – higher than 37% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available – fixed in 3.1.0

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:L/A:N

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Changed
Confidentiality: None
Integrity: Low
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: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Changed: An exploited vulnerability can affect resources beyond the security scope managed by the security authority of the vulnerable component. In this case, the vulnerable component and the impacted component are different and managed by different security authorities.
Confidentiality: None: There is no loss of confidentiality within the impacted component.
Integrity: Low: Modification of data is possible, but the attacker does not have control over the consequence of a modification, or the amount of modification is limited. The data modification does not have a direct, serious impact on 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 23 Jun 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 26 Oct 2026.

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 or the CVSS exploit maturity 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

Upgrade lfprojects MLflow to 3.1.0.

Affected software

Product	Affected	Fixed in
PyPImlflow`pkg:pypi/mlflow`Other threats	Affected <3.1.0	Fixed in 3.1.0

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

AI-suggested

Immediate mitigation should include restricting network access to MLflow instances, ensuring that only trusted internal users and systems can reach the MLflow server, preferably via VPN or secure internal networks.
Implement strict network segmentation and firewall rules to prevent MLflow servers from making arbitrary outbound requests to internal or sensitive network segments.
Monitor and log all proxy requests made by MLflow’s gateway_proxy_handler to detect unusual or unauthorized request patterns indicative of SSRF exploitation attempts.
If you cannot patch yet, consider disabling or restricting the functionality of the gateway_proxy_handler if feasible, or apply custom validation on the gateway_path parameter at the application or proxy level to whitelist allowed paths and destinations.
Conduct thorough security reviews of MLflow deployment configurations, ensuring minimal exposure of MLflow services to the public internet and enforcing strong authentication and authorization controls.
Prepare to apply vendor patches promptly once available and test them in staging environments to ensure no disruption to ML workflows.
Educate DevOps and security teams about SSRF risks in ML platforms and incorporate SSRF detection in security monitoring tools.

Description

gateway_proxy_handler in MLflow before 3.1.0 lacks gateway_path validation.

Impact

For European organizations, the SSRF vulnerability in MLflow could lead to unauthorized internal network reconnaissance and potential manipulation of internal services, especially in environments where MLflow is integrated with sensitive machine learning workflows or data pipelines. Organizations relying on MLflow for critical AI/ML operations may face risks of integrity compromise, such as unauthorized triggering or modification of model training, deployment processes, or internal API calls. This could disrupt AI model lifecycle management or introduce malicious data or commands into workflows. While no direct data leakage or service disruption is indicated, the ability to reach internal services could facilitate lateral movement or further exploitation by attackers. Given the increasing adoption of MLflow in industries like finance, healthcare, and manufacturing across Europe, the vulnerability poses a moderate risk to operational integrity and trustworthiness of AI systems. Additionally, organizations with MLflow instances exposed to the internet or insufficiently segmented internal networks are at higher risk. The lack of required privileges or user interaction lowers the barrier for exploitation, increasing the urgency for mitigation in environments where MLflow is deployed.

CWE-918Server-Side Request Forgery (SSRF) (opens in a new tab)
Base weaknessCWE Top 25 (2025): #22OWASP Top 10 A01:2025: Broken Access Control (opens in a new tab)
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.
OWASP Cheat Sheets
Server Side Request Forgery Prevention (opens in a new tab)
Unvalidated Redirects and Forwards (opens in a new tab)
Cross Site Scripting Prevention (opens in a new tab)
DOM based XSS Prevention (opens in a new tab)
Authorization (opens in a new tab)
OWASP ASVS 4.0.3
V12.6.1 (opens in a new tab)
V5.2.6 (opens in a new tab)
Attack patterns
Server Side Request Forgery (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 23 Jun 2025, 14:57 UTC

WeaknessesCWE-918

Technical details

References

Community reviews

Related threats