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CVE-2025-23331: Critical Memory Allocation Vulnerability in NVIDIA Triton Inference Server

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Overview

The NVIDIA Triton Inference Server, a popular platform for deploying AI models, is susceptible to a critical vulnerability, CVE-2025-23331. This vulnerability affects both Windows and Linux versions of the server and could potentially lead to a system compromise or data leakage. The vulnerability enables a user to trigger a memory allocation with an excessively large size value, causing a segmentation fault by providing an invalid request.

Vulnerability Summary

CVE ID: CVE-2025-23331
Severity: Critical (7.5 CVSS Score)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: Denial of service, potential system compromise, and data leakage

Affected Products

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Product | Affected Versions

NVIDIA Triton Inference Server for Windows | All Versions
NVIDIA Triton Inference Server for Linux | All Versions

How the Exploit Works

The exploit takes advantage of the server’s failure to validate and properly handle the size value of a user’s request. By providing an invalid request with an excessively large size value, the user can trigger a segmentation fault. This fault can lead to a denial of service and, in certain circumstances, allow for further exploitation that could result in system compromise or data leakage.

Conceptual Example Code

Below is a conceptual example of how the vulnerability might be exploited. This is a sample HTTP request with a malicious payload designed to trigger a segmentation fault.

POST /api/v1/inference HTTP/1.1
Host: target.example.com
Content-Type: application/json
{ "data_size": "99999999999999999999999999999", "data": "malicious_data" }

Mitigation Guidance

Users are strongly advised to apply the vendor patch as soon as it becomes available. Until then, the use of a Web Application Firewall (WAF) or Intrusion Detection System (IDS) can serve as a temporary mitigation measure.

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Disclaimer:

The information and code presented in this article are provided for educational and defensive cybersecurity purposes only. Any conceptual or pseudocode examples are simplified representations intended to raise awareness and promote secure development and system configuration practices.

Do not use this information to attempt unauthorized access or exploit vulnerabilities on systems that you do not own or have explicit permission to test.

Ameeba and its authors do not endorse or condone malicious behavior and are not responsible for misuse of the content. Always follow ethical hacking guidelines, responsible disclosure practices, and local laws.

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