MLflow: Remote code execution on unauthenticated MLflow servers via third-party scorer deserialization importing caller-placed artifact modules
CVSS score
9.8
EPSS probability
—
Risk score
1.0
Published
3 h ago
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Impact on My Inventory
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Vulnerability description
DayBreach AI Explanation
Verified against sourcesSources read
1
Source agreement
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Evidence confidence
%55
Short explanation
A vulnerability in the third-party scorer deserialization mechanism of MLflow allows remote code execution on unauthenticated servers.
It poses a critical risk by allowing unauthorized code execution without credentials on deployments where authentication is disabled.
What should be done?
- Follow vendor advisories and updates
- Review write permissions for server processes
- Enable authentication mechanisms
Chain of evidence and sources
MLflow 3.12.0rc0 ile 3.17.0 arasındaki sürümlerde üçüncü taraf değerlendirici seri durumdan çıkarma işlemleri uzaktan kod yürütülmesine izin verir.
Kimlik doğrulaması devre dışı bırakılmış MLflow dağıtımları, kimlik bilgisi gerektirmeden bu kod yürütme açığına zemin hazırlar.
Show the technical text from the sources
MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.12.0rc0 until 3.17.0, third-party scorer deserialization in mlflow/genai/scorers/base.py accepts request-selected descendants of THIRD_PARTY_SCORER_ALLOWED_MODULES rather than requiring an exact allowed module. A caller can use MLflow artifact endpoints to place a Python module beneath a writable allowed package and submit its module and class through /ajax-api/3.0/mlflow/scorer/invoke. Scorer.model_validate_json() imports the module and constructs the class before trace processing. Authentication-disabled deployments permit this code execution without credentials, while basic-auth deployments require a principal capable of creating an experiment and run and uploading artifacts. In either configuration, the allowed scorer package must be writable by the server process. This issue is fixed in version 3.17.0.
CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
CWE: CWE-94, CWE-470
This explanation was compiled automatically from the source records. Scores, active exploitation, patch and exploit status come straight from the sources.
IMPACT: Affected Products and Software
RED HAT CSAF/VEX: Product Impact Check
SCORING: CVSS from Multiple Sources
CVSS Scores
The Common Vulnerability Scoring System is a standardised framework for assessing the severity of vulnerabilities in software and systems. We collect and display CVSS scores from several sources for each CVE.
| SCORE | VERSION | SEVERITY | VECTOR | SOURCE |
|---|---|---|---|---|
| 9.8 | CVSS V3_1 | CRITICAL | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H | CVE CNA |
| 9.8 | CVSS V31 | CRITICAL | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H | NVD |
| 9.8 | CVSS 3.x | — | Vector not published | GitHub Advisory |
| 0.0 | CVSS 4.0 | — | Vector not published | GitHub Advisory |
Patch and Remediation Guide
Solution & Remediation Advisory
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Public exploit and active exploitation status
Verified exploit intelligence
No verified public PoC, exploit repository or Metasploit module has been found for this CVE yet.
Weakness Classification and Attack Patterns (CWE & CAPEC)
CWE - Common Weakness Enumeration
While CVE identifies specific instances of vulnerabilities, CWE categorises the common flaws or weaknesses that can lead to vulnerabilities. CVE-2026-116692 is associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of the common attributes and approaches employed by adversaries to exploit the CVE-2026-116692 weaknesses.
Attack Vector and Accessibility
Official Sources and Advisories
Similar Vulnerabilities
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