Gradio Server Side Request Forgery Scanner

Gradio is a popular open-source tool used for creating Machine Learning web applications. Developed by Gradio Project, it allows researchers and developers to demonstrate and interact with machine learning models through a web interface. Gradio is used in a variety of fields, including data science, AI research, and online collaboration platforms for machine learning projects. Users can easily share their models with others via auto-generated links, enabling broader access to machine learning tools. The tool is also integrated with platforms like Hugging Face to streamline the deployment and sharing of AI models. The flexibility and ease of use make Gradio a widely adopted solution for machine learning application development.

The Server-Side Request Forgery (SSRF) vulnerability allows attackers to make unauthorized requests from the server hosting the application. SSRF vulnerabilities often enable cybercriminals to access internal systems and services that would otherwise be inaccessible from the external network. Exploiting SSRF can result in internal data exposure, unauthorized third-party service interactions, and potentially accessing sensitive information. Due to its nature, SSRF could be a stepping stone for more severe attacks if coupled with other vulnerabilities or insecure configurations. Organizations must adequately secure applications to mitigate the risks associated with SSRF attacks. Early detection and patching of SSRF vulnerabilities are essential to protect against malicious exploitation.

In Gradio, the SSRF vulnerability primarily affects the component server endpoint, which processes specific user requests. This endpoint does not properly validate user-supplied data in the request bodies, specifically allowing manipulation of the 'data' parameter. This lack of validation permits attackers to supply arbitrary URLs, causing the server to initiate requests to potential malicious sites. The vulnerability operates within the functions designed for temporary file downloads, like 'download_temp_copy_if_needed.' Technical exploitation involves targeting unauthenticated endpoints, making it relatively straightforward. These vulnerabilities can enable attackers to probe internal networks or access sensitive data without direct access to the server.

Exploiting the SSRF vulnerability in Gradio can lead to several potential impacts. Attackers can reconnoiter the internal network, potentially identifying other services and systems to exploit. They could indirectly perform actions on behalf of the server, such as retrieving or manipulating data from internal sources. There's also the risk of leaking sensitive or confidential information, which could be leveraged in subsequent attacks. Besides direct data exposure, adversaries might establish a foothold for lateral movement within the network. These actions may lead to broader security breaches, emphasizing the critical nature of addressing SSRF vulnerabilities.

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/