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。
| 厂商 | 产品 | 版本范围 | 状态 |
|---|---|---|---|
| mlflow | mlflow/mlflow | unspecified< 3.10.0 |
affected |
尽管我们使用了先进的大模型技术,但其输出仍可能包含不准确或过时的信息。神龙努力确保数据的准确性,但请您根据实际情况进行核实和判断。
| 厂商 | 产品 | 影响版本 | CPE | 订阅 |
|---|---|---|---|---|
| mlflow | mlflow/mlflow | unspecified ~ 3.10.0 | - |
|
| # | POC 描述 | 源链接 | 神龙链接 |
|---|---|---|---|
| 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详情 |
未找到公开 POC。
登录以生成 AI POC暂无评论