mlflow
Behoben in
2.9.3
2.10.0
CVE-2024-27133 describes a critical Cross-Site Scripting (XSS) vulnerability discovered in MLflow, a platform for managing the machine learning lifecycle. This flaw arises from insufficient sanitization of dataset table fields within MLflow recipes, leading to potential client-side Remote Code Execution (RCE) when recipes are executed in Jupyter Notebook using untrusted datasets. The vulnerability affects MLflow versions 2.9.2 and earlier; a fix is available in version 2.10.0.
The primary impact of CVE-2024-27133 is the potential for client-side Remote Code Execution (RCE) within Jupyter Notebook environments. An attacker can craft a malicious dataset that, when used in an MLflow recipe, injects JavaScript code into the notebook's output. This code executes in the context of the user's browser, granting the attacker control over the user's session and potentially access to sensitive data. The attack vector involves leveraging MLflow's recipe execution functionality with untrusted datasets, highlighting the importance of input validation and sanitization. Successful exploitation could lead to data theft, credential compromise, and further lateral movement within the affected environment.
CVE-2024-27133 was publicly disclosed on February 23, 2024. While no active exploitation campaigns have been publicly confirmed, the CRITICAL severity and the potential for RCE make it a high-priority vulnerability. The vulnerability's reliance on recipe execution and Jupyter Notebook integration suggests a targeted attack scenario. The availability of a public proof-of-concept is likely, increasing the risk of exploitation.
Organizations heavily reliant on MLflow for machine learning workflows, particularly those using Jupyter Notebooks and incorporating external or untrusted datasets into their recipes, are at significant risk. Teams using shared MLflow instances or those with less stringent data governance practices are also more vulnerable.
• python / mlflow:
import mlflow
# Check MLflow version
print(mlflow.__version__)
# Check for suspicious recipe configurations or dataset sources
# Review Jupyter Notebook logs for unusual JavaScript execution• generic web: • Check for unusual JavaScript execution in Jupyter Notebook logs. • Monitor for suspicious network activity originating from Jupyter Notebook processes.
disclosure
Exploit-Status
EPSS
0.20% (43% Perzentil)
CVSS-Vektor
The primary mitigation for CVE-2024-27133 is to upgrade MLflow to version 2.10.0 or later, which includes the necessary sanitization fixes. If upgrading immediately is not feasible, consider implementing strict input validation on dataset table fields within your MLflow recipes. Additionally, restrict access to MLflow recipes to trusted users and datasets. Employ a Web Application Firewall (WAF) with XSS filtering rules to block malicious payloads. Monitor MLflow logs for suspicious activity, particularly related to recipe execution and dataset handling.
Aktualisieren Sie MLflow auf eine Version nach 2.9.2. Dies kann mit `pip install --upgrade mlflow` erfolgen. Stellen Sie sicher, dass die Aktualisierung erfolgreich war.
Schwachstellenanalysen und kritische Warnungen direkt in deinen Posteingang.
CVE-2024-27133 is a critical XSS vulnerability in MLflow versions up to 2.9.2. It allows attackers to inject malicious JavaScript code when running recipes with untrusted datasets, potentially leading to RCE in Jupyter Notebook.
You are affected if you are using MLflow version 2.9.2 or earlier and are running recipes with datasets from untrusted sources in a Jupyter Notebook environment.
Upgrade MLflow to version 2.10.0 or later to address the insufficient sanitization issue. If immediate upgrade is not possible, restrict the use of untrusted datasets in recipes.
While no active exploitation has been confirmed, the vulnerability's criticality and ease of exploitation make it a likely target for attackers. Monitoring for suspicious activity is recommended.
Refer to the MLflow security advisory for detailed information and updates: [https://mlflow.org/docs/security](https://mlflow.org/docs/security)
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