CVE-2024-1183 Scanner

Gradio is a software tool used widely across many industries to build user interfaces for machine learning applications. It allows users to create interfaces easily and share machine learning models quickly in interactive web applications. The platform is popular among data scientists and developers for its ease of use and versatility, offering robust capabilities in deploying live machine learning demonstrations. Typically, Gradio is used for applications that need to process data inputs and deliver real-time computational outputs. Its integration capacity makes it useful in research, development, and educational settings where interactive demonstrations of AI models are needed. Through Gradio, users can capture the power of Python-based models and present them seamlessly through web-based interfaces to a broader audience.

Server-Side Request Forgery (SSRF) vulnerabilities allow attackers to send crafted requests from a vulnerable server to internal, unauthorized, or third-party systems. This enables attackers to exploit the known fact that many internal resources are trusted and accessible by the application server itself. An SSRF vulnerability exploits the server-side functionality in the way that it can handle external requests that an attacker controls or manipulates. Such vulnerabilities can allow malicious activities like scanning internal IPs and ports, accessing cloud instances, or exploiting internal machines that aren't directly exposed to the internet. Attackers can leverage SSRF to bypass network controls or security units like firewalls and sensitive configurations by issuing requests manipulated to their benefit. Thus, SSRF is often used as a gateway for further compromise into secure computational networks.

In this specific SSRF vulnerability within Gradio, the exploit occurs through manipulating the 'file' parameter. By crafting a specific GET request where 'file' points to a controlled URL, an attacker can discern additional internal network information. This discovery occurs by analyzing response elements such as the presence of a 'Location' header or responding with 'File not allowed' errors, which provide clues about the status of the service and its configuration. This vulnerability is particularly concerning as it can expose sensitive internal systems inadvertently allowing attackers unauthorized access pathways. The SSRF vulnerability identified here leverages headers and error codes, making it subtle and potentially impactful when effectively crafted and executed progressively.

If an SSRF vulnerability within Gradio is exploited, attackers might gain unauthorized access to internal network resources, leading to potential data breaches. They could perform malicious activities such as unauthorized scanning, retrieving sensitive information or configuration details, and, worst-case scenarios, pivoting deeper into a network for further exploitation. It may also lead to exposure of attack surfaces previously protected by internal-only network configurations. Such an exploit could eventually result in system downtimes or critical data exposures if chained with other active vulnerabilities. Overall, SSRF can open pathways for complex attack scenarios that compromise confidentiality, integrity, and availability of backend systems if undetected.

REFERENCES

• https://github.com/gradio-app/gradio/commit/2ad3d9e7ec6c8eeea59774265b44f11df7394bb4
• https://huntr.com/bounties/103434f9-87d2-42ea-9907-194a3c25007c