CVE-2025-0453VulnerabilityPublished 20 Mar 2025Updated 15 Oct 2025

CVE-2025-0453: mlflow/mlflow · Insufficient Resource Pool

Affects mlflow/mlflowSource CVE Database V5

Not known to be exploited · 11% EPSS · CVSS 5.9 Medium.

Data as of 26 Oct 2026, 06:26 UTC

CVSS 3.0 base score

5.9Medium

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

EPSS: 11.0% chance of exploitation in 30 days – higher than 95% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

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

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

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: High: A successful attack depends on conditions beyond the attacker's control. That is, a successful attack cannot be accomplished at will, but requires the attacker to invest in some measurable amount of effort in preparation or execution against the vulnerable component before a successful attack can be expected. For example, a successful attack may require an attacker to: gather knowledge about the environment in which the vulnerable target/component exists; prepare the target environment to improve exploit reliability; or inject themselves into the logical network path between the target and the resource requested by the victim in order to read and/or modify network communications (e.g., a man in the middle attack).
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: 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: None: There is no loss of confidentiality within the impacted component.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: High: There is total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 20 Mar 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 2026-10-26.

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
PyPImlflow`pkg:pypi/mlflow`Other threats	Affected =2.17.2	Fixed in–

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AI-suggested

To mitigate this vulnerability, organizations should implement strict rate limiting on the /graphql endpoint to restrict the number of queries per user or IP address within a given timeframe. Deploying resource quotas or limits on worker threads/processes dedicated to handling GraphQL queries can prevent resource exhaustion. Monitoring and anomaly detection systems should be configured to identify unusual query patterns or spikes in requests targeting experiment runs. If possible, restrict access to the MLflow service to trusted internal networks or VPNs to reduce exposure. Employ Web Application Firewalls (WAFs) with custom rules to block or throttle suspicious GraphQL query payloads. Organizations should also track updates from the MLflow project for patches or security advisories addressing this issue and plan timely application of fixes once available. Finally, consider segmenting MLflow infrastructure to isolate critical components and minimize the blast radius of potential DoS attacks.

Description

In mlflow/mlflow version 2.17.2, the `/graphql` endpoint is vulnerable to a denial of service attack. An attacker can create large batches of queries that repeatedly request all runs from a given experiment. This can tie up all the workers allocated by MLFlow, rendering the application unable to respond to other requests. This vulnerability is due to uncontrolled resource consumption.

Impact

For European organizations leveraging MLflow in their AI/ML workflows, this vulnerability poses a significant risk to service availability. Disruption of MLflow services can halt machine learning experiment tracking, model versioning, and deployment pipelines, potentially delaying critical AI-driven business processes. Industries such as finance, healthcare, automotive, and manufacturing, which increasingly rely on ML models for decision-making and automation, could face operational downtime and productivity loss. Additionally, organizations with MLflow exposed to the internet or accessible by multiple users are at higher risk of remote exploitation. Although the vulnerability does not compromise data confidentiality or integrity, the denial of service could indirectly affect business continuity and service level agreements. The absence of known exploits reduces immediate risk, but the medium severity and ease of remote access warrant proactive mitigation to prevent potential attacks.

CWE-410Insufficient Resource Pool (opens in a new tab)
Class weakness
The product's resource pool is not large enough to handle peak demand, which allows an attacker to prevent others from accessing the resource by using a (relatively) large number of requests for resources.
Architecture and Design
Do not perform resource-intensive transactions for unauthenticated users and/or invalid requests.
Architecture and Design
Consider implementing a velocity check mechanism which would detect abusive behavior.
Operation
Consider load balancing as an option to handle heavy loads.
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

huntr.com/bounties/788327ec-714a-4d5c-83aa-8df04dd7612b (opens in a new tab)

Community reviews

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 15 Oct 2025, 13:05 UTC

WeaknessesCWE-410

Technical details

References

Community reviews

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