Vulnerability-Lookup

FKIE_CVE-2026-4035

Vulnerability from fkie_nvd - Published: 2026-06-03 09:16 - Updated: 2026-08-14 13:19

Severity

7.7 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N
7.7 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N

Summary

A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server's environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0.

References

URL	Tags
security@huntr.dev	https://github.com/mlflow/mlflow/commit/4a3f2f720cb4f058c9e0c5b883e0acc9ab64a7f3	Patch
security@huntr.dev	https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233	Exploit, Third Party Advisory
0b0ca135-0b70-47e7-9f44-1890c2a1c46c	https://access.redhat.com/security/cve/CVE-2026-4035
0b0ca135-0b70-47e7-9f44-1890c2a1c46c	https://bugzilla.redhat.com/show_bug.cgi?id=2484318
134c704f-9b21-4f2e-91b3-4a467353bcc0	https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233	Exploit, Third Party Advisory
0b0ca135-0b70-47e7-9f44-1890c2a1c46c	https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-4035.json

Impacted products

	Vendor	Product	Version
	lfprojects	mlflow	*

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "mlflow/mlflow",
          "vendor": "mlflow",
          "versions": [
            {
              "lessThan": "3.11.0",
              "status": "affected",
              "version": "unspecified",
              "versionType": "custom"
            }
          ]
        }
      ],
      "source": "security@huntr.dev"
    },
    {
      "affectedData": [
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-mlflow-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-th06-cpu-torch210-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-th06-cuda130-torch210-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-th06-rocm64-torch291-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-training-cuda128-torch29-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-workbench-codeserver-datascience-cpu-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        },
        {
          "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
          "cpes": [
            "cpe:/a:redhat:openshift_ai"
          ],
          "defaultStatus": "unaffected",
          "packageName": "rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9",
          "product": "Red Hat OpenShift AI (RHOAI)",
          "vendor": "Red Hat"
        }
      ],
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "6EFB4C88-58E2-416A-95A7-FA6C4CDF4288",
              "versionEndExcluding": "3.11.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server\u0027s environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0."
    },
    {
      "lang": "es",
      "value": "Una vulnerabilidad en las versiones de mlflow/mlflow anteriores a la 3.11.0 permite la resoluci\u00f3n de variables de entorno en secretos de AI Gateway, lo que puede ser explotado para exfiltrar credenciales de entorno sensibles del lado del servidor a un punto final controlado por un atacante. Este problema surge porque el campo \u0027api_key\u0027 en los secretos del gateway puede aceptar referencias a \u0027$ENV_VAR\u0027, que se resuelven contra el entorno del servidor de MLflow durante el tiempo de ejecuci\u00f3n. Los secretos resueltos se env\u00edan entonces en los encabezados de autenticaci\u00f3n del proveedor al \u0027api_base\u0027 ascendente configurado. Esta vulnerabilidad puede ser explotada por usuarios autenticados con bajos privilegios en implementaciones con \u0027basic-auth\u0027 o por usuarios no autenticados en implementaciones predeterminadas sin \u0027basic-auth\u0027. El impacto incluye la posible fuga de credenciales sensibles, como credenciales de artefactos en la nube (\u0027AWS_ACCESS_KEY_ID\u0027, \u0027AWS_SECRET_ACCESS_KEY\u0027), lo que podr\u00eda llevar al envenenamiento de artefactos y a la ejecuci\u00f3n de c\u00f3digo transfronteriza en entornos descendentes. El problema est\u00e1 solucionado en la versi\u00f3n 3.11.0."
    }
  ],
  "id": "CVE-2026-4035",
  "lastModified": "2026-08-14T13:19:00.193",
  "metrics": {
    "cvssMetricV30": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "LOW",
          "baseScore": 9.1,
          "baseSeverity": "CRITICAL",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "LOW",
          "privilegesRequired": "LOW",
          "scope": "CHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:L",
          "version": "3.0"
        },
        "exploitabilityScore": 3.1,
        "impactScore": 5.3,
        "source": "security@huntr.dev",
        "type": "Secondary"
      }
    ],
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 7.7,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "CHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 3.1,
        "impactScore": 4.0,
        "source": "nvd@nist.gov",
        "type": "Primary"
      },
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 7.7,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "CHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 3.1,
        "impactScore": 4.0,
        "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-4035",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "partial"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-06-03T13:10:20.303201Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-06-03T09:16:13.083",
  "references": [
    {
      "source": "security@huntr.dev",
      "tags": [
        "Patch"
      ],
      "url": "https://github.com/mlflow/mlflow/commit/4a3f2f720cb4f058c9e0c5b883e0acc9ab64a7f3"
    },
    {
      "source": "security@huntr.dev",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233"
    },
    {
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
      "url": "https://access.redhat.com/security/cve/CVE-2026-4035"
    },
    {
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2484318"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233"
    },
    {
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-4035.json"
    }
  ],
  "sourceIdentifier": "security@huntr.dev",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-201"
        }
      ],
      "source": "security@huntr.dev",
      "type": "Secondary"
    },
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-201"
        }
      ],
      "source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
      "type": "Secondary"
    }
  ]
}

CVE-2026-4035 (GCVE-0-2026-4035)

Vulnerability from cvelistv5 – Published: 2026-06-03 07:18 – Updated: 2026-08-14 12:04

Title

Environment Variable Resolution Vulnerability in mlflow/mlflow

Summary

Severity

9.1 (Critical)


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

7.7 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N

SSVC

Exploitation: poc Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-06-03 13:10 UTC

CWE

CWE-201 - Insertion of Sensitive Information Into Sent Data

Assigner

@huntr_ai mitre root

References

5 references

URL	Tags
https://huntr.com/bounties/f8e591a0-0f19-4910-b82…
https://github.com/mlflow/mlflow/commit/4a3f2f720…
https://access.redhat.com/security/cve/CVE-2026-4035	vdb-entryx_refsource_REDHAT
https://bugzilla.redhat.com/show_bug.cgi?id=2484318	issue-trackingx_refsource_REDHAT
https://security.access.redhat.com/data/csaf/v2/v…	x_sadp-csaf-vex

Impacted products

2 products

Vendor	Product	Version
mlflow	mlflow/mlflow	Affected: unspecified , < 3.11.0 (custom)
Red Hat	Red Hat OpenShift AI (RHOAI)	cpe:/a:redhat:openshift_ai

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2026-4035",
                "options": [
                  {
                    "Exploitation": "poc"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2026-06-03T13:10:20.303201Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2026-06-03T13:10:24.407Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "references": [
          {
            "tags": [
              "exploit"
            ],
            "url": "https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233"
          }
        ],
        "title": "CISA ADP Vulnrichment"
      },
      {
        "affected": [
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-mlflow-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-th06-cpu-torch210-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-th06-cuda130-torch210-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-th06-rocm64-torch291-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-training-cuda128-torch29-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-workbench-codeserver-datascience-cpu-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          },
          {
            "collectionURL": "https://access.redhat.com/downloads/content/package-browser/",
            "cpes": [
              "cpe:/a:redhat:openshift_ai"
            ],
            "defaultStatus": "unaffected",
            "packageName": "rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9",
            "product": "Red Hat OpenShift AI (RHOAI)",
            "vendor": "Red Hat"
          }
        ],
        "datePublic": "2026-06-03T07:18:08.512Z",
        "descriptions": [
          {
            "lang": "en",
            "value": "A flaw was found in MLflow. This vulnerability allows an attacker to exfiltrate sensitive server-side environment credentials. It occurs because the AI Gateway secrets can resolve environment variables, which are then sent to an attacker-controlled endpoint. This could lead to unauthorized access to cloud resources and potentially enable cross-boundary code execution."
          }
        ],
        "metrics": [
          {
            "other": {
              "content": {
                "namespace": "https://access.redhat.com/security/updates/classification/",
                "value": "Important"
              },
              "type": "Red Hat severity rating"
            }
          },
          {
            "cvssV3_1": {
              "attackComplexity": "LOW",
              "attackVector": "NETWORK",
              "availabilityImpact": "NONE",
              "baseScore": 7.7,
              "baseSeverity": "HIGH",
              "confidentialityImpact": "HIGH",
              "integrityImpact": "NONE",
              "privilegesRequired": "LOW",
              "scope": "CHANGED",
              "userInteraction": "NONE",
              "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N",
              "version": "3.1"
            },
            "format": "CVSS"
          }
        ],
        "problemTypes": [
          {
            "descriptions": [
              {
                "cweId": "CWE-201",
                "description": "Insertion of Sensitive Information Into Sent Data",
                "lang": "en",
                "type": "CWE"
              }
            ]
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2026-08-14T12:04:28.457Z",
          "orgId": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c",
          "shortName": "redhat-SADP"
        },
        "references": [
          {
            "tags": [
              "vdb-entry",
              "x_refsource_REDHAT"
            ],
            "url": "https://access.redhat.com/security/cve/CVE-2026-4035"
          },
          {
            "name": "RHBZ#2484318",
            "tags": [
              "issue-tracking",
              "x_refsource_REDHAT"
            ],
            "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2484318"
          },
          {
            "tags": [
              "x_sadp-csaf-vex"
            ],
            "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-4035.json"
          }
        ],
        "timeline": [
          {
            "lang": "en",
            "time": "2026-06-03T09:00:55.993Z",
            "value": "Reported to Red Hat."
          },
          {
            "lang": "en",
            "time": "2026-06-03T07:18:08.512Z",
            "value": "Made public."
          }
        ],
        "title": "python-mlflow: MLflow: Sensitive credential exfiltration via environment variable resolution in AI Gateway secrets",
        "x_adpType": "supplier",
        "x_generator": {
          "engine": "sadp-cli 1.0.0"
        }
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "mlflow/mlflow",
          "vendor": "mlflow",
          "versions": [
            {
              "lessThan": "3.11.0",
              "status": "affected",
              "version": "unspecified",
              "versionType": "custom"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server\u0027s environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0."
        }
      ],
      "metrics": [
        {
          "cvssV3_0": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "LOW",
            "baseScore": 9.1,
            "baseSeverity": "CRITICAL",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "LOW",
            "privilegesRequired": "LOW",
            "scope": "CHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:L",
            "version": "3.0"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-201",
              "description": "CWE-201 Insertion of Sensitive Information Into Sent Data",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2026-06-03T07:18:08.512Z",
        "orgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
        "shortName": "@huntr_ai"
      },
      "references": [
        {
          "url": "https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233"
        },
        {
          "url": "https://github.com/mlflow/mlflow/commit/4a3f2f720cb4f058c9e0c5b883e0acc9ab64a7f3"
        }
      ],
      "source": {
        "advisory": "f8e591a0-0f19-4910-b82e-16c9956f2233",
        "discovery": "EXTERNAL"
      },
      "title": "Environment Variable Resolution Vulnerability in mlflow/mlflow"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "c09c270a-b464-47c1-9133-acb35b22c19a",
    "assignerShortName": "@huntr_ai",
    "cveId": "CVE-2026-4035",
    "datePublished": "2026-06-03T07:18:08.512Z",
    "dateReserved": "2026-03-12T02:17:42.523Z",
    "dateUpdated": "2026-08-14T12:04:28.457Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2"
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.