Описание
MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim.
This issue affects MLflow version through 3.10.1
A flaw was found in MLflow, a platform for managing the machine learning lifecycle. This Stored Cross-Site Scripting (XSS) vulnerability, a type of injection where malicious scripts are injected into trusted websites, is caused by the unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can exploit this by uploading a malicious MLmodel file containing a payload. When another user views this artifact in the user interface, the payload executes, potentially leading to session hijacking or unauthorized operations on behalf of the victim.
Отчет
Moderate: This Stored Cross-Site Scripting (XSS) flaw in MLflow, as deployed in Red Hat OpenShift AI, stems from insecure parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file; subsequent viewing by another user executes the payload, potentially leading to session hijacking or unauthorized actions within the victim's session.
Меры по смягчению последствий
Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability.
Затронутые пакеты
| Платформа | Пакет | Состояние | Рекомендация | Релиз |
|---|---|---|---|---|
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-mlflow-rhel9 | Fix deferred | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9 | Fix deferred | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9 | Fix deferred | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9 | Fix deferred | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9 | Fix deferred | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9 | Fix deferred | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9 | Not affected | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-th06-cpu-torch210-py312-rhel9 | Fix deferred | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-th06-cuda130-torch210-py312-rhel9 | Fix deferred | ||
| Red Hat OpenShift AI (RHOAI) | rhoai/odh-th06-rocm64-torch291-py312-rhel9 | Fix deferred |
Показывать по
Дополнительная информация
Статус:
EPSS
4.6 Medium
CVSS3
Связанные уязвимости
MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim. This issue affects MLflow version through 3.10.1
MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface
EPSS
4.6 Medium
CVSS3