CVE-2025-54381

July 30, 2025, 3:15 p.m.

9.9
Critical

Description

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. In versions 1.4.0 until 1.4.19, the file upload processing system contains an SSRF vulnerability that allows unauthenticated remote attackers to force the server to make arbitrary HTTP requests. The vulnerability stems from the multipart form data and JSON request handlers, which automatically download files from user-provided URLs without validating whether those URLs point to internal network addresses, cloud metadata endpoints, or other restricted resources. The documentation explicitly promotes this URL-based file upload feature, making it an intended design that exposes all deployed services to SSRF attacks by default. Version 1.4.19 contains a patch for the issue.

Product(s) Impacted

Vendor Product Versions
Bentoml
  • Bentoml
  • 1.4.0-1.4.19

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-918
Server-Side Request Forgery (SSRF)
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.

*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 bentoml bentoml 1.4.0-1.4.19 / / / / / / /

CVSS Score

9.9 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: July 29, 2025, 11:15 p.m.
Last Modified: July 30, 2025, 3:15 p.m.

Status : Received

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.