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Mlflow EUVDEUVD-2025-209100

| CVE-2025-15381 HIGH
Information Exposure (CWE-200)
2026-03-27 @huntr_ai GHSA-g6pg-52vf-843h
7.1
CVSS 3.1 · NVD
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Vulnerability Assessment

Weakness
CWE
OWASP Top 10:2025
A01:2025 - Broken Access Control
ASVS 5.0
V14 - Data Protection Data exposure prevention
MITRE ATT&CK

2 techniques across 2 tactics: Collection, Discovery

The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up.

Impact

The CVSS 3.0 vector (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N) indicates network-accessible exploitation with low complexity requiring only low-privilege authenticated access, resulting in high confidentiality and integrity impacts. Real-world risk is moderate-to-high for organizations using MLflow's basic-auth feature in multi-tenant environments where experiment isolation is critical. The PR:L requirement means attackers need valid credentials, reducing the attack surface compared to unauthenticated vulnerabilities. No EPSS score was provided in the intelligence data, and no confirmed active exploitation (CISA KEV) or public proof-of-concept exists at time of analysis. The vulnerability was disclosed through a responsible disclosure platform (HuntrDev), suggesting coordinated disclosure. Priority should be elevated for deployments where unauthorized trace access could expose proprietary model architectures, training data references, or competitive intelligence. Deployments not using the basic-auth application or operating in single-tenant environments face reduced risk.

Exploit ScenarioDerived

An authenticated attacker with a low-privilege account on a shared MLflow deployment leverages direct API calls to tracing endpoints, bypassing experiment-level permission checks. The attacker enumerates trace IDs from experiments they do not have access to, retrieves sensitive trace metadata revealing model architecture details, hyperparameters, and training pipeline information belonging to other teams. Additionally, the attacker creates fraudulent assessments on competitor traces to inject misleading evaluation data or disrupt model selection workflows. No public exploit identified at time of analysis, but the low attack complexity (AC:L) and network accessibility (AV:N) make exploitation straightforward for authenticated users with basic API knowledge.

This Week Schedule remediation this week. Why: HIGH severity (CVSS 7.0-8.9).
No patch listed yet 0.0% exploitation probability (EPSS)

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AnalysisAIv1 – Mar 27, 2026

MLflow's basic-auth authentication system fails to protect tracing and assessment endpoints, enabling any authenticated user with no experiment permissions to read trace metadata and create unauthorized assessments. The vulnerability affects MLflow deployments running with the '--app-name=basic-auth' flag and carries a CVSS score of 8.1 (High) with network-based attack vector requiring low privilege authentication. This vulnerability was reported via the HackerOne bug bounty platform (@huntr_ai) with no public exploit identified at time of analysis.

RemediationAI

Consult the HuntrDev bounty disclosure at https://huntr.com/bounties/149fb2f9-ef4b-4136-a25c-20563451904c for vendor-released patch information and specific version guidance. As an interim mitigation, implement network-layer access controls to restrict access to MLflow tracing and assessment API endpoints (/api/2.0/mlflow/traces/* and related assessment paths) to only trusted administrator IP ranges. Consider disabling the basic-auth application if multi-tenant permission enforcement is not required, or implement reverse proxy authentication with explicit endpoint-level authorization rules that enforce experiment-level permissions before proxying requests to MLflow. Review audit logs for any unauthorized trace reads or assessment creation activity by users lacking experiment permissions to identify potential exploitation. Organizations should subscribe to MLflow security advisories and monitor the GitHub repository for security-related commits addressing this permission bypass.

Affected VersionsENISA

mlflow/mlflow unspecified ≤latest

Affected Packages

pypi mlflow no fix

Source: OSV (Open Source Vulnerabilities)

Affects your stack

Affects 1 package in pypi

Checking this CVE against your own stacks and servers is part of the Basic plan and up.

Technical ContextAI · show details

MLflow is an open-source platform for managing machine learning lifecycle, including experimentation, reproducibility, and deployment. The affected component is the basic-auth application module, which implements permission-based access controls for experiment resources. The vulnerability stems from CWE-200 (Exposure of Sensitive Information to an Unauthorized Actor), where specific API endpoints for tracing functionality and assessment creation bypass the permission validation layer. The CPE identifier (cpe:2.3:a:mlflow:mlflow/mlflow:*:*:*:*:*:*:*:*) indicates broad version impact across the MLflow product line. Tracing endpoints expose metadata about ML experiment execution flows, while assessment endpoints allow annotation and evaluation of traces, both of which should be protected by the role-based access control system when basic-auth is enabled.

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  • Exploit and PoC references, threat intelligence from 22 sources

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Severity by source

NVD PRIMARY 7.1 HIGH
AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:H/A:N
Red Hat 8.1 HIGH

Primary rating from NVD.

CVSS VectorNVD

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

Lifecycle Timeline

2
CVSS changed
Apr 28, 2026 - 14:37 NVD
8.1 (HIGH) 7.1 (HIGH)
CVE Published
Mar 27, 2026 - 16:17 nvd
HIGH 8.1

DescriptionNVD

In the latest version of mlflow/mlflow, when the basic-auth app is enabled, tracing and assessment endpoints are not protected by permission validators. This allows any authenticated user, including those with NO_PERMISSIONS on the experiment, to read trace information and create assessments for traces they should not have access to. This vulnerability impacts confidentiality by exposing trace metadata and integrity by allowing unauthorized creation of assessments. Deployments using mlflow server --app-name=basic-auth are affected.

Blast Radius

ecosystem impact
† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth
  • 2 pypi packages depend on mlflow (2 direct, 0 indirect)

Ecosystem-wide dependent count for version 3.8.1.

Executive ViewAI

Why It Matters

MLflow deployments using basic authentication are vulnerable to unauthorized access to experiment metadata and fraudulent assessment creation by any authenticated user, regardless of their assigned permissions. This allows insider threats or compromised low-privilege accounts to extract sensitive ML model tracking data and manipulate assessment records, creating compliance and model integrity risks for organizations relying on MLflow for ML operations.

Business Impact: High
Compensating Controls

Implement Web Application Firewall (WAF) rules to restrict access to /traces and /assessments endpoints by IP allowlist; enforce network segmentation limiting MLflow endpoint access to authorized subnets; disable basic-auth in favor of stronger authentication methods (LDAP, SAML, OAuth) if supported by your MLflow version; enable detailed audit logging of all endpoint access and assessment creation; restrict experiment creation and management permissions at the application level to reduce lateral movement from low-privilege accounts.

Threat Intelligence Overview

EPSS Score
0.0%
Top 99% exploitable
Threat Intel
1
1 source
Data Quality
7 sources
Confidence: High
EUVD NVD CVE.org EPSS Vulnrichment GHSA Vendor VulDB

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity HIGH
CISA KEV No
Public POC No
EPSS 0.0%
Patch available No
Active threat No
Deciding rule
HIGH severity (CVSS 7.0-8.9)
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision
Scheduled
Exploitation
Poc
System Exposure· derived
Open
Automatable
No
Human Impact
High
Technical Impact
Total

Priority Score

36
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +36
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.

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