Overview
The cybersecurity landscape has been hit by yet another substantial security flaw, this time in the realm of Artificial Intelligence (AI) applications and online serving systems. The vulnerability, identified as CVE-2025-54381, affects BentoML, a widely used Python library that streamlines the process of building machine learning models for AI applications. The flaw is significant due to BentoML’s prevalent usage in the AI field, with the potential to compromise numerous AI applications and online serving systems.
The core of this issue lies in an SSRF vulnerability found within BentoML’s file upload processing system, which allows unauthenticated remote attackers to manipulate the server into making arbitrary HTTP requests. This could lead to system compromises or data leakage, highlighting the severity of the threat.
Vulnerability Summary
CVE ID: CVE-2025-54381
Severity: Critical (9.9 CVSS Score)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: System compromise, potential data leakage
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Product | Affected Versions
BentoML Python Library | 1.4.0 to 1.4.19
How the Exploit Works
The flaw resides in the multipart form data and JSON request handlers of the BentoML library. These handlers automatically download files from user-provided URLs without running any validation checks on whether these URLs point to internal network addresses, cloud metadata endpoints, or other restricted resources. This lack of validation enables an attacker to craft malicious URLs that could force the server to make arbitrary HTTP requests, potentially leading to SSRF attacks.
Conceptual Example Code
Here’s a conceptual example of how this vulnerability might be exploited:
POST /file/upload HTTP/1.1
Host: target.example.com
Content-Type: multipart/form-data
{ "file_url": "http://internal.network/sensitive/data" }In this example, the attacker uses a crafted HTTP POST request to the server’s file upload endpoint, providing a URL (`http://internal.network/sensitive/data`) that points to a restricted resource on the internal network. The server, lacking proper validation, could then unwittingly download and expose sensitive data.
Prevention and Mitigation
The BentoML team has already issued a patch in the 1.4.19 version that addresses this vulnerability. Therefore, users are strongly advised to update their BentoML Python library to the latest version. As a temporary mitigation, users can apply a Web Application Firewall (WAF) or Intrusion Detection System (IDS) to help detect and prevent potential SSRF attacks. However, these measures should be seen as temporary solutions, and the patch should be applied as soon as possible.