CVE-2024-47871

Oct. 17, 2024, 5:11 p.m.

9.1
Critical

Description

Gradio is an open-source Python package designed for quick prototyping. This vulnerability involves **insecure communication** between the FRP (Fast Reverse Proxy) client and server when Gradio's `share=True` option is used. HTTPS is not enforced on the connection, allowing attackers to intercept and read files uploaded to the Gradio server, as well as modify responses or data sent between the client and server. This impacts users who are sharing Gradio demos publicly over the internet using `share=True` without proper encryption, exposing sensitive data to potential eavesdroppers. Users are advised to upgrade to `gradio>=5` to address this issue. As a workaround, users can avoid using `share=True` in production environments and instead host their Gradio applications on servers with HTTPS enabled to ensure secure communication.

Product(s) Impacted

Vendor Product Versions
Gradio Project
  • Gradio
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-311
Missing Encryption of Sensitive Data
The product does not encrypt sensitive or critical information before storage or transmission.

*CPE(s)

Affected systems and software identified for this CVE.

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

CVSS Score

9.1 / 10

CVSS Data - 3.1

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Privileges Required: NONE
  • Scope: UNCHANGED
  • Confidentiality Impact: HIGH
  • Integrity Impact: HIGH
  • Availability Impact: NONE
  • CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N

    View Vector String

Timeline

Published: Oct. 10, 2024, 11:15 p.m.
Last Modified: Oct. 17, 2024, 5:11 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.