Gradio Local File Inclusion Scanner

Gradio is an open-source UI library that allows developers to create web-based interfaces that enable users to interact with machine learning models directly. It is widely used in the AI and ML community, providing an easy-to-use framework for creating demos and interactive applications. Users can drag and drop datasets, fill forms, and test the output of their machine learning models in real-time. While primarily used by developers working with Python, Gradio can also be integrated into other frameworks, making it a versatile tool for showcasing models. The library streamlines complex ML implementations into streamlined, user-friendly web applications. Gradio's intuitive interface facilitates collaboration between developers and non-developers, enhancing the accessibility and visibility of machine learning advancements.

The Local File Inclusion (LFI) vulnerability allows attackers to exploit Gradio's component server to read files from the host system. This type of vulnerability can occur when an application includes files without adequately validating input, potentially allowing attackers to access sensitive information on the server. Specifically, the vulnerability takes advantage of a method call in the Gradio components class that mishandles file paths. By manipulating parameters, attackers can traverse directories and read arbitrary files. This flaw compromises the confidentiality of the system by exposing sensitive files to unauthorized users. As a result, the vulnerability poses significant security risks to applications running vulnerable versions of the software.

Local File Inclusion in Gradio occurs due to improper validation of file paths in the component server's method calls. The vulnerable endpoint accepts user input, which can be exploited to construct paths leading to sensitive files. Attackers can craft payloads that abuse this feature by specifying paths like `/etc/passwd` on Unix systems or `c:\\windows\\win.ini` on Windows systems. The parameter manipulation allows attackers to access file content, which is then returned in the response body upon a successful match. Multiple checks are implemented to confirm the exploitation, such as status codes and body content matching. The vulnerability allows malicious users to perform unauthorized file access, leading to potential data breaches and compromise of confidential information.

If exploited by malicious actors, this vulnerability could result in sensitive data exposure, including system files and potentially user credentials. Attackers may leverage this access to escalate privileges, launch further attacks, or manipulate the application's behavior. The organization might face legal and compliance issues, particularly regarding data protection standards. Furthermore, unauthorized access to sensitive files could disrupt service operations and damage the reputation of companies relying on Gradio for ML demonstration. Consequently, it is critical to address this vulnerability to maintain the security of systems using the Gradio library.

REFERENCES

• https://github.com/gradio-app/gradio/commit/24a583688046867ca8b8b02959c441818bdb34a2
• https://www.horizon3.ai/attack-research/disclosures/exploiting-file-read-vulnerabilities-in-gradio-to-steal-secrets-from-hugging-face-spaces/