MLflow是MLflow开源的一个简化机器学习开发的平台,包括跟踪实验、将代码打包成可重复的运行以及共享和部署模型。 mlflow 3.9.0及之前版本存在安全漏洞,该漏洞源于Flask和FastAPI认证机制架构不匹配,导致认证绕过,可能导致未认证远程攻击者提交作业、读取作业结果、取消运行中作业以及向实验注入任意跟踪数据。
| Vendor | Product | Version Range | Status |
|---|---|---|---|
| mlflow | mlflow/mlflow | unspecified< 3.10.0 |
affected |
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| Vendor | Product | Affected Versions | CPE | Subscribe |
|---|---|---|---|---|
| mlflow | mlflow/mlflow | unspecified ~ 3.10.0 | - |
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| # | POC Description | Source Link | Shenlong Link |
|---|---|---|---|
| 1 | A vulnerability in mlflow/mlflow versions 3.9.0 and earlier allows unauthenticated access to certain FastAPI routes when the server is started with authentication enabled (`--app-name basic-auth`) and served via uvicorn (ASGI). The FastAPI permission middleware only enforces authentication on `/gateway/` routes, leaving other routes such as the Job API (`/ajax-api/3.0/jobs/*`) and the OpenTelemetry trace ingestion API (`/v1/traces`) unprotected. This allows unauthenticated remote attackers to submit jobs, read job results, cancel running jobs, and inject arbitrary trace data into experiments. The issue arises from an architectural mismatch between Flask and FastAPI authentication mechanisms, where the `_find_fastapi_validator()` function fails to handle non-`/gateway/` paths, resulting in a complete authentication bypass. This vulnerability is fixed in version 3.10.0. | https://github.com/projectdiscovery/nuclei-templates/blob/main/http/cves/2026/CVE-2026-2652.yaml | POC Details |
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