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

Vulnerability from cvelistv5 – Published: 2026-06-03 07:18 – Updated: 2026-08-14 12:04
VLAI Previdian
Title
Environment Variable Resolution Vulnerability in mlflow/mlflow
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.
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 CNA under the mitre root
CNA scorecard C 65/100 over 40 records in the last 180 days details
References
Impacted products
Vendor Product Version CPE status
mlflow mlflow/mlflow Affected: unspecified , < 3.11.0 (custom)
guessed Create a notification for this product.
Red Hat Red Hat OpenShift AI (RHOAI)     cpe:/a:redhat:openshift_ai
Create a notification for this product.
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: c13a62e7-9000-4684-8c6d-c387aa83475a

Vulnerability ID: CVE-2026-4035

Status: Confirmed

Status Updated: 2026-11-03 09:11 UTC

Exploited: Yes

Timestamps
First Seen: 2026-11-03
Asserted: 2026-11-03
Scope
Notes: Environment Variable Resolution Vulnerability in mlflow/mlflow | Affected: Mlflow / mlflow/mlflow | CVSS: 7.7 (HIGH) | EPSS: 0.00521 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence

Type: Public Report

Signal: Successful Exploitation

Confidence: 70%

Source: previdian

Details
Feed Previdian (previdian.com)
Title Environment Variable Resolution Vulnerability in mlflow/mlflow
Cve Id CVE-2026-4035
Vendor Mlflow
Ghsa Id None
Product mlflow/mlflow
Added Date 2026-11-03T09:11:07.347Z
Cvss Score 7.7
Epss Score 0.00521
Previous Ids
Cvss Severity HIGH
Virtual Patch False
Cvss Estimated False
Epss Percentile 0.44283
Used In Malware unknown
Vulnerability Id CVE-2026-4035
Ahead Of Cisa Kev None
Not Yet In Cisa Kev True
References
Created: 2026-11-04 10:00 UTC | Updated: 2026-11-04 10:00 UTC
Show details on NVD website
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",
  "vulnerability-lookup:meta": {
    "epss": {
      "cve": "CVE-2026-4035",
      "date": "2026-11-04",
      "epss": "0.00521",
      "percentile": "0.44361"
    },
    "nvd": {
      "cve": {
        "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"
          }
        ]
      }
    },
    "redhat_vex": {
      "aggregate_severity": "Important",
      "current_release_date": "2026-08-13T17:09:37+00:00",
      "cve": "CVE-2026-4035",
      "id": "CVE-2026-4035",
      "initial_release_date": "2026-06-03T07:18:08.512000+00:00",
      "product_status:known_not_affected": "19",
      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "python-mlflow: MLflow: Sensitive credential exfiltration via environment variable resolution in AI Gateway secrets",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-4035.json",
      "version": "3"
    },
    "vulnrichment": {
      "containers": {
        "adp": [
          {
            "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"
            }
          },
          {
            "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": "2026-06-03T13:09:52.628Z",
              "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"
          }
        ],
        "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"
    }
  }
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.




Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

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.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON
How durations are calculated

Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.

Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.

Loading timeline…




    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.