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CVE-2025-23327: Integer Overflow Vulnerability in NVIDIA Triton Inference Server

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Overview

A critical vulnerability, designated as CVE-2025-23327, has been identified in NVIDIA’s Triton Inference Server for Windows and Linux. This vulnerability can lead to an integer overflow if exploited, potentially causing denial of service and data tampering. This issue is particularly concerning as it can affect critical systems and lead to potential system compromise or data leakage if not addressed promptly.

Vulnerability Summary

CVE ID: CVE-2025-23327
Severity: High (CVSS: 7.5)
Attack Vector: Network
Privileges Required: None
User Interaction: None
Impact: Denial of service and data tampering leading to potential system compromise or data leakage

Affected Products

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Product | Affected Versions

NVIDIA Triton Inference Server for Windows | All pre-patch versions
NVIDIA Triton Inference Server for Linux | All pre-patch versions

How the Exploit Works

The exploit leverages the lack of proper input validation in the NVIDIA Triton Inference Server. An attacker can craft specific inputs that cause an integer overflow within the server’s processing component. This overflow can lead to unpredictable server behavior, which may include crashes (leading to a denial of service) and potential data tampering.

Conceptual Example Code

A conceptual example of an exploit might be a specially crafted JSON payload like the following:

POST /infer HTTP/1.1
Host: target.example.com
Content-Type: application/json
{
"inputs": [
{
"name": "input0",
"datatype": "INT32",
"shape": [0, -2147483648]
}
]
}

In this example, the `shape` array contains an extremely large negative integer, which may cause an integer overflow if the server does not properly validate and handle the input.

Mitigation

Users are advised to apply the vendor-supplied patch as soon as possible. If a patch cannot be applied immediately, implementing Web Application Firewalls (WAF) or Intrusion Detection Systems (IDS) can provide temporary mitigation.

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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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