MediumCVE-2026-116691

    MLflow CrewAI autologging records LLM provider API keys in logged trace spans (CVE-2026-116691)

    Published Last modified Sources: BSI, CVE.org, OSV, Hersteller

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    What is affected, what to do

    MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 2.19.0 until 3.17.0, mlflow.crewai.autolog() records CrewAI Agent objects without consistently sanitizing nested credential-bearing values. mlflow/crewai/autolog.py::_get_agent_attributes() converts the Agent llm field to a string that includes the provider API key, while mlflow/crewai/autolog.py::_construct_full_inputs() expands the Agent object into mlflow.spanInputs.

    Update MLflow to version 3.17.0, mlflow to version 3.17.0.

    Affected
    • MLflow gte 2.19.0, < 3.17.0..*

    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: BSI, CVE.org, OSV, Hersteller

    0.2 %

    EPSS score

    Low — model estimates < 1% exploitation likelihood.

    Source: FIRST EPSS, percentile 10.9

    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
    63.46 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
    no public PoC known
    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
    0.2 %
    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
    10.9
    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
    CISA assessment (SSVC)
    Exploitation: proof of concept · not automatable · Technical impact: partial
    Source: CISA Vulnrichment, as of 3 November 2026
    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-200, CWE-312, CWE-532

    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 2.19.0 until 3.17.0, mlflow.crewai.autolog() records CrewAI Agent objects without consistently sanitizing nested credential-bearing values. mlflow/crewai/autolog.py::_get_agent_attributes() converts the Agent llm field to a string that includes the provider API key, while mlflow/crewai/autolog.py::_construct_full_inputs() expands the Agent object into mlflow.spanInputs. Applications can therefore send plaintext provider credentials to the MLflow trace backend or a configured OTLP destination, where trace readers can obtain them. This issue is fixed in version 3.17.0.

    Source: BSI CSAF, BSI WID Portal, NVD (NIST), OSV (Open Source Vulnerabilities), GitHub Security Advisories, cvelistv5

    Affected products

    The following versions are affected. Older releases of the same product line are also vulnerable unless stated otherwise.

    Affected products
    Affected versionsFixed in
    mlflowmlflow
    • ≥ 2.19.0, < 3.17.0
    • —
    Open SourceMLflow
    • < 3.17.0
    • 3.17.0
    pipmlflow
    • >= 2.19.0, < 3.17.0
    • 3.17.0
    pypimlflow
    • 3.3.0
    • 2.20.3
    • 3.3.2
    • 3.7.0rc0
    • 3.16.1
    • 3.1.3
    • 3.9.0
    • 2.20.0
    • 2.22.4
    • 2.20.1
    • 3.6.0rc0
    • 3.15.2
    • 3.1.0rc0
    • 2.21.0
    • 3.3.0rc0
    • 3.15.0
    • 3.10.0
    • 2.21.2
    • 3.0.0rc3
    • 2.22.0rc0
    • 3.1.4
    • 2.21.0rc0
    • 3.6.0
    • 3.1.0
    • 3.5.0rc0
    • 3.0.0rc1
    • 2.20.4
    • 3.16.0
    • 3.1.1
    • 3.4.0
    • 3.11.0rc0
    • 3.0.0
    • 2.22.5
    • 3.10.0rc0
    • 2.22.2
    • 2.20.0rc0
    • 3.5.0
    • 3.4.0rc0
    • 2.21.3
    • 3.8.0rc0
    • 3.12.0
    • 3.0.0rc0
    • 2.22.0
    • 3.9.0rc0
    • 3.11.0rc1
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    Vendors and products

    Weakness classes (CWE)

    • CWE-200Exposure of Sensitive Information to an Unauthorized Actor

      Class

      The product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.

      Entry at MITRE CWE-200

    • CWE-312Cleartext Storage of Sensitive Information

      Base

      The product stores sensitive information in cleartext within a resource that might be accessible to another control sphere.

      Entry at MITRE CWE-312

    • CWE-532Insertion of Sensitive Information into Log File

      Base

      The product writes sensitive information to a log file.

      Entry at MITRE CWE-532

    References & sources

    • github.commlflow/mlflow/commit/20927930d27f0072fcfeb7f024f24ad8c5e77c47x_refsource_MISC, web
    • github.commlflow/mlflow/pull/25845x_refsource_MISC, web
    • github.commlflow/mlflow/releases/tag/v3.17.0x_refsource_MISC, web
    • github.commlflow/mlflow/security/advisories/GHSA-j7qq-vr8c-xvw3x_refsource_CONFIRM, 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).

    1. Record modifiedCISA ADP

      CISA assessment (SSVC): exploitation: proof of concept, automatable: no

      Show 2 original changes
      Individual changes of this event as published by NVD
      TypeActionBeforeAfter
      Referenceadded
      —
      https://github.com/mlflow/mlflow/security/advisories/GHSA-j7qq-vr8c-xvw3
      SSVC assessmentadded
      —
      {"id":"CVE-2026-116691","role":"CISA Coordinator","options":[{"exploitation":"poc"},{"automatable":"no"},{"technicalImpact":"partial"}],"version":"2.0.3","timestamp":"2026-11-03T12:09:11.249947Z"}

      Source identifier: 134c704f-9b21-4f2e-91b3-4a467353bcc0

    2. ReceivedGitHub

      CVSS V3.1: AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N

      Weakness type: CWE-200, CWE-532, CWE-312

      Description added or changed

      Show 10 original changes
      Individual changes of this event as published by NVD
      TypeActionBeforeAfter
      Descriptionadded
      —
      MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 2.19.0 until 3.17.0, mlflow.crewai.autolog() records CrewAI Agent objects without consistently sanitizing nested credential-bearing values. mlflow/crewai/autolog.py::_get_agent_attributes() converts the Agent llm field to a string that includes the provider API key, while mlflow/crewai/autolog.py::_construct_full_inputs() expands the Agent object into mlflow.spanInputs. Applications can therefore send plaintext provider credentials to the MLflow trace backend or a configured OTLP destination, where trace readers can obtain them. This issue is fixed in version 3.17.0.
      CVSSadded
      —
      AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
      Weakness type (CWE)added
      —
      CWE-200
      Weakness type (CWE)added
      —
      CWE-532
      Weakness type (CWE)added
      —
      CWE-312
      Affected versions (publisher)added
      —
      New affected value received. <a href="https://github.com/CVEProject/cvelistV5/blob/main/cves/2026/116xxx/CVE-2026-116691.json">CVE-2026-116691</a>
      Referenceadded
      —
      https://github.com/mlflow/mlflow/commit/20927930d27f0072fcfeb7f024f24ad8c5e77c47
      Referenceadded
      —
      https://github.com/mlflow/mlflow/pull/25845
      Referenceadded
      —
      https://github.com/mlflow/mlflow/releases/tag/v3.17.0
      Referenceadded
      —
      https://github.com/mlflow/mlflow/security/advisories/GHSA-j7qq-vr8c-xvw3

      Source identifier: security-advisories@github.com

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