CVE-2023-47115 Scanner

The Label Studio is a data labeling tool used by organizations and individuals to annotate various forms of data. Widely recognized for its versatility, it supports text, audio, images, and video annotations, allowing diverse use across industries such as data science, machine learning, and artificial intelligence research. With an easy-to-use interface, it is a preferred choice among data scientists, researchers, and developers tasked with preparing datasets for training machine learning models. The platform is designed to enhance the productivity of teams handling large datasets by providing an efficient annotation and review environment. Its adaptability to a wide range of annotation tasks makes it a robust tool for creating high-quality labeled datasets. Label Studio integrates easily with popular machine learning frameworks and data processing scripts, ensuring seamless operation within various workflows.

The Cross-Site Scripting (XSS) vulnerability detected in Label Studio allows malicious actors to execute arbitrary JavaScript on users’ browsers. Such a security flaw emerges when a crafted image file, uploaded as an avatar by an authenticated user, is rendered as HTML containing malicious scripts. This flaw can empower attackers to perform unauthorized actions, such as stealing user cookies, altering webpage content, or even redirecting users to malicious sites. XSS vulnerabilities are particularly dangerous as they give attackers the potential to control affected users’ behavior on the website. Providing generally limitless scripting capabilities, successful exploitation can lead to significant breaches in user data confidentiality. It raises serious security implications and jeopardizes the integrity and trustworthiness of web interactions.

Technically, the vulnerability arises at the point where Label Studio handles file uploads for user avatars. The raw HTTP request allows for crafted image files to be uploaded, which are then interpreted and rendered as HTML. This error is exacerbated when such files contain embedded scripts that intend to exploit the website's users. The endpoint in question lacks sufficient validation checks to prevent the embedded scripts from executing. This oversight can be specifically targeted to execute any JavaScript, potentially compromising user interactions and data. Furthermore, as the vulnerability is contingent on users viewing a malicious avatar, it predicates significant risk upon common user behavior patterns on the platform.

Exploitation of this XSS vulnerability can result in dire consequences, ranging from data theft to unauthorized actions taken in the end-user's context. The breach enables attackers to impersonate users or hijack their sessions, leading to potential disclosure of confidential information. Additionally, the attacker could modify page contents, thus misleading end-users, and possibly propagating further attacks by redirecting users to malicious sites. The broader implications also include damaging the platform's reputability and user trust. In extreme cases, users' machines could be further compromised through additional scripts executed within the browser context.

REFERENCES

• https://github.com/advisories/GHSA-q68h-xwq5-mm7x
• https://docs.djangoproject.com/en/4.2/ref/views/#serving-files-in-development
• https://github.com/HumanSignal/label-studio/blob/1.8.2/label_studio/users/functions.py#L18-L49
• https://github.com/HumanSignal/label-studio/blob/1.8.2/label_studio/users/urls.py#L25-L26
• https://nvd.nist.gov/vuln/detail/CVE-2023-47115