CVE-2024-47870

Oct. 17, 2024, 4:57 p.m.

8.1
High

Description

Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves a **race condition** in the `update_root_in_config` function, allowing an attacker to modify the `root` URL used by the Gradio frontend to communicate with the backend. By exploiting this flaw, an attacker can redirect user traffic to a malicious server. This could lead to the interception of sensitive data such as authentication credentials or uploaded files. This impacts all users who connect to a Gradio server, especially those exposed to the internet, where malicious actors could exploit this race condition. Users are advised to upgrade to `gradio>=5` to address this issue. There are no known workarounds for this issue.

Product(s) Impacted

Vendor Product Versions
Gradio Project
  • Gradio
  • *

Weaknesses

CWE-362
Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')
The product contains a code sequence that can run concurrently with other code, and the code sequence requires temporary, exclusive access to a shared resource, but a timing window exists in which the shared resource can be modified by another code sequence that is operating concurrently.

*CPE(s)

Type Vendor Product Version Update Edition Language Software Edition Target Software Target Hardware Other Information
a gradio_project gradio / / / / / python / /

CVSS Score

8.1 / 10

CVSS Data

  • Attack Vector: NETWORK
  • Attack Complexity: HIGH
  • Privileges Required: NONE
  • Scope: UNCHANGED
  • Confidentiality Impact: HIGH
  • Integrity Impact: HIGH
  • Availability Impact: HIGH
  • View Vector String

    CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H

Date

  • Published: Oct. 10, 2024, 11:15 p.m.
  • Last Modified: Oct. 17, 2024, 4:57 p.m.

Status : Analyzed

CVE has been recently published to the CVE List and has been received by the NVD.

More info

Source

security-advisories@github.com

*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.