CVE-2023-47117 Scanner

Label Studio is an open-source data labeling tool widely used by machine learning practitioners and data scientists to create, manage, and annotate large datasets. This software streamlines the annotation process and enables collaboration among team members, leveraging the Django web framework to provide a robust backend. Label Studio supports various data types, including text, images, and audio, which makes it versatile for different machine learning projects. It is deployed broadly in both academic research and industry applications, facilitating the training of machine learning models by providing high-quality labeled data. Developers and researchers use it to manage labeling tasks efficiently, improving the precision of machine learning algorithms through well-defined datasets.

The Information Disclosure vulnerability in Label Studio occurs due to the exploitation of Django's Object Relational Mapper (ORM). This vulnerability allows an attacker to construct a query filter chain to gain unauthorized access to sensitive information within the user accounts. The ORM filter manipulation makes it possible for attackers to extract sensitive fields character by character. Such vulnerabilities are particularly concerning, as they might lead to significant data leaks if exploited maliciously. Label Studio's improper handling of filters within its ORM layer constitutes a critical security risk, potentially compromising user privacy and data integrity. Ensuring ORM filters are correctly managed is crucial to preventing Information Disclosure vulnerabilities in complex web applications.

In technical terms, the vulnerability manifests through specific HTTP requests that exploit filter operations in the Label Studio API. The endpoint vulnerable to this attack allows for manipulation of filters related to tasks and projects. The crucial parameter lies within the "filters" field as defined in the JSON payload of the patch request. By strategically designing these filter queries, an attacker can retrieve sensitive data that was not intended to be exposed. Vulnerability scanning tools can detect such exposures by analyzing HTTP responses for unauthorized data disclosure. This intricate exploitation method requires understanding Django's ORM filters and the ability to manipulate them to bypass standard security controls.

If left unremediated, this vulnerability could lead to severe consequences, such as data breaches where an attacker gains access to confidential information. Potential impacts include leakage of user credentials or other personal information, compromising the security and privacy of affected users. Once sensitive information is disclosed, it may be used for initiating further attacks such as identity theft, financial fraud, or unauthorized system access. The reputational damage to the organization deploying Label Studio may result, deterring future collaboration or data sharing initiatives. Therefore, addressing this vulnerability is crucial in maintaining the integrity and confidentiality of datasets within any machine learning application using Label Studio.

REFERENCES

• https://security.snyk.io/vuln/SNYK-PYTHON-LABELSTUDIO-6056277
• https://nvd.nist.gov/vuln/detail/CVE-2023-47117
• https://github.com/elttam/publications