MLflow: Unsafe Reflection (CVE-2026-115886)
Published Last modified Sources: CVE.org, BSI, OSV, Hersteller
What is affected, what to do
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, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments.
An exploit is publicly available. Update MLflow to version 3.17.0, mlflow to version 3.17.0.
- Affected
- MLflow
gte 3.12.0rc0, < 3.17.0..*
- MLflow
Not sure whether and how you are affected? Ask a question about this vulnerability
Metrics
6.5
Priority
XIEM Effective CVSS: highest available CVSS rating from NVD and vendor data.
Source: Vendor (CNA), CVSS v3
6.5
CVSS v3
Severity Medium on the scale from 0 to 10.
Source: CVE.org, BSI, OSV, Hersteller
0.3 %
EPSS score
Low — model estimates < 1% exploitation likelihood.
Source: FIRST EPSS, percentile 25.3
Show all metrics
- Severity
- Medium
Priority (0–325)
The priority score is a consolidated metric that condenses several individual signals into a single, action-guiding ranking: technical severity (CVSS), exploitation likelihood (EPSS), known-exploited status (KEV) and the context of the affected asset. The aim is to answer the only truly important question in vulnerability management: what first?
Read in glossary- 67.60 Methodology
Exploit maturity
An exploit is code or a technique that specifically uses a concrete vulnerability to force unintended behaviour — such as executing code or escalating privileges. It is the tool that turns a theoretical flaw into a practical attack.
Read in glossary- PoC (publicly reported)
XIEM Effective CVSS
XIEM® is the tiered managed-security operating model from NEOSEC and a registered trademark of NEOSEC GmbH. It extends classic SIEM — the central collection, normalisation and correlation of security-relevant events — with endpoint forensics, network visibility and deception technology, and consolidates them into three building tiers: Sentry, Orchestrate and Command.
Read in glossary- 6.5 Methodology
CVSS v3
CVSS is the globally established, open standard for assessing the technical severity of a vulnerability on a scale from 0 to 10. It describes how easily a flaw can be exploited and what damage it can cause — but not how likely its actual exploitation is. CVSS and the complementary EPSS deliberately answer different questions.
Read in glossary- 6.5
EPSS score
The EPSS score is the absolute model output for an individual vulnerability: the estimated probability of real-world exploitation within the next 30 days, expressed as a value between 0 and 1 (i.e. 0 % to 100 %). A score of 0.42 means „the model estimates a 42 % chance of real-world exploitation within 30 days“.
Read in glossary- 0.3 %
- Low — model estimates < 1% exploitation likelihood.
EPSS percentile
The EPSS percentile is the relative position of a vulnerability within all EPSS-scored CVEs, expressed as a value between 0 and 1. A percentile of 0.90 means „this CVE has a higher EPSS score than 90 % of all other scored CVEs“. Unlike the EPSS score, it is not a probability — but a positioning within a ranking.
Read in glossary- 25.3
- Elevated — this CVE ranks above at least 10% of all CVEs scored today.
KEV status
The KEV catalogue is a list maintained by the US authority CISA of vulnerabilities for which active exploitation in the wild has been confirmed. It answers not the question „how severe?“ but „is it already being attacked?“ — thereby sharply prioritising patching.
Read in glossary- —
- Published
CWE
CWE is a catalogue of typical weakness types in software and hardware — such as flawed input validation, unsafe memory handling or poor authentication. Where a CVE names a concrete vulnerability in a concrete product, a CWE describes the underlying class of defect.
Read in glossary- CWE-470, CWE-918
Detection rules
No NEOSEC detection rule is available for this vulnerability yet.
Classification under NIS2 and CRA
Rule-based classification from exploitation status and severity. It does not replace a case-by-case assessment.
NIS2
NIS2 is EU Directive (EU) 2022/2555 to raise the level of cybersecurity across the Union. It substantially broadens obligations compared with the first NIS directive: more sectors, mandatory risk management, staged reporting duties and personal accountability of senior management.
Read in glossary- Handle the vulnerability in regular vulnerability handling under Art. 21(2)(e) NIS2.
CRA
The CRA is EU Regulation (EU) 2024/2847 setting horizontal cybersecurity requirements for „products with digital elements“ — from industrial controllers and software to connected consumer devices. For the first time manufacturers must ensure security across the entire product lifecycle, handle vulnerabilities and provide security updates.
Read in glossary- From 11.12.2027, Annex I Part II CRA requires manufacturers of affected products with digital elements to handle vulnerabilities without delay and provide security updates.
ISO/IEC 27001
ISO/IEC 27001 is the internationally leading standard for an information security management system (ISMS). It describes a risk-based, process-oriented framework to systematically plan, implement, review and improve information security.
Read in glossary- Control A.8.8, management of technical vulnerabilities: assess relevance to your systems, plan remediation and keep evidence.
Description
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, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0.
Source: NVD (NIST), BSI CSAF, BSI WID Portal, OSV (Open Source Vulnerabilities), cvelistv5, CVE.org (MITRE), GitHub Security Advisories
Affected products
The following versions are affected. Older releases of the same product line are also vulnerable unless stated otherwise.
| Affected versions | Fixed in | ||
|---|---|---|---|
| mlflow | mlflow |
|
|
| Open Source | MLflow |
|
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| pip | mlflow |
|
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| pypi | mlflow |
|
|
Vendors and products
Weakness classes (CWE)
CWE-470Use of Externally-Controlled Input to Select Classes or Code ('Unsafe Reflection')
Base
The product uses external input with reflection to select which classes or code to use, but it does not sufficiently prevent the input from selecting improper classes or code.
Entry at MITRE CWE-470CWE-918Server-Side Request Forgery (SSRF)
Base
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.
Entry at MITRE CWE-918
Public exploit references
Publicly reported proof-of-concepts, exploit modules and detection templates for this vulnerability. NEOSEC links to the original sources only and does not provide any exploit code.
| CIRCL Sightings | github.com (opens in a new tab) | PoC (publicly reported)reported, not verified |
References & sources
- github.commlflow/mlflow/security/advisories/GHSA-2wqp-q3c6-ccj5x_refsource_CONFIRM, web
- github.commlflow/mlflow/pull/25890x_refsource_MISC, web
- github.commlflow/mlflow/commit/bac30b4148c4f40045b4d4db07ae60ab3838803fx_refsource_MISC, web
- github.commlflow/mlflow/releases/tag/v3.17.0x_refsource_MISC, web
- github.commlflow/mlflowpackage
Changes to this CVE
Who changed this CVE and when: NVD (analysis, affected products, CVSS), the CVE publisher (CNA) and additional providers such as CISA (ADP).
ReceivedGitHub
CVSS V3.1: AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N
Weakness type: CWE-918, CWE-470
Description added or changed
Show 9 original changes
Individual changes of this event as published by NVD Type Action Before After Description added —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, the scorer invoke, register, and GenAI evaluate endpoints, including unauthenticated POST /ajax-api/3.0/mlflow/scorer/invoke, accept serialized third-party scorer data that Scorer.model_validate_json resolves using a module-path allowlist without constraining the selected class or its constructor arguments. When optional trulens and trulens-providers-litellm packages are installed, an attacker can select mlflow.genai.scorers.trulens, choose a model URI outside MLflow's native provider registry so execution uses the LiteLLM fallback, and supply api_base and api_key values that cause the server to send an outbound request to an attacker-chosen or internal host with attacker-controlled headers and body. The vulnerable deserialization occurs before a tracking-store lookup or job submission. This issue is fixed in version 3.17.0.CVSS added —AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:NWeakness type (CWE) added —CWE-918Weakness type (CWE) added —CWE-470Affected versions (publisher) added —New affected value received. <a href="https://github.com/CVEProject/cvelistV5/blob/main/cves/2026/115xxx/CVE-2026-115886.json">CVE-2026-115886</a>Reference added —https://github.com/mlflow/mlflow/commit/bac30b4148c4f40045b4d4db07ae60ab3838803fReference added —https://github.com/mlflow/mlflow/pull/25890Reference added —https://github.com/mlflow/mlflow/releases/tag/v3.17.0Reference added —https://github.com/mlflow/mlflow/security/advisories/GHSA-2wqp-q3c6-ccj5Source identifier: security-advisories@github.com
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<a href="https://intel.neosec.eu/en/cve/CVE-2026-115886"><img src="https://intel.neosec.eu/badge/cve/CVE-2026-115886.svg?lang=en" alt="CVE-2026-115886 – NEOSEC Intel" height="20"></a>