Ameeba Security Research

Defensive CVE and exploit intelligence

Ameeba Blog Search
TRENDING · 1 WEEK
Attack Vector
Vendor
Severity

CVE-2023-31036: NVIDIA Triton Inference Server Path Traversal Vulnerability

Overview

CVE-2023-31036 is a severe vulnerability discovered in NVIDIA Triton Inference Server for Linux and Windows that may lead to a potential system compromise or data leakage. The security flaw allows an attacker, using a specific command line option, to exploit a relative path traversal vulnerability. This vulnerability is of significant concern as it affects a wide range of NVIDIA Triton Inference Server versions and can lead to serious outcomes such as code execution, denial of service, privilege escalation, information disclosure, and data tampering.

Vulnerability Summary

CVE ID: CVE-2023-31036
Severity: High (7.5 CVSS)
Attack Vector: Network
Privileges Required: Low
User Interaction: None
Impact: Successful exploitation may lead to code execution, denial of service, privilege escalation, information disclosure, and data tampering.

Affected Products

Ameeba Chat Icon Share secrets securely

Ameeba is private infrastructure for communication and sensitive work built on encrypted identity instead of exposed corporate identity systems.

Passwords, credentials, confidential files, screenshots, internal discussions, sensitive AI context, and private coordination should not become exposed across ordinary communication platforms.

  • • Encrypted identity
  • • Private Spaces for organizations and teams
  • • End-to-end encrypted chat, calls, files, and notes
  • • Sensitive AI work and protected collaboration
  • • Built for information that cannot leak

Our mission is to secure human work alongside AI.

Product | Affected Versions

NVIDIA Triton Inference Server for Linux | All versions prior to patch
NVIDIA Triton Inference Server for Windows | All versions prior to patch

How the Exploit Works

The exploit works when the NVIDIA Triton Inference Server is launched with the non-default command-line option –model-control explicit. This allows an attacker to use the model load API to instigate a relative path traversal. The attacker can manipulate the file path input to access, modify, or execute files outside of the intended directory.

Conceptual Example Code

Here is a conceptual example of how this vulnerability could be exploited. This is a HTTP request that uses the model load API to load a malicious model from a location outside of the intended directory.

POST /v1/models/load HTTP/1.1
Host: target.example.com
Content-Type: application/json
{
"model_name": "../../../etc/passwd",
"version": "1.0"
}

In this example, the relative path traversal is used to upload a model file that points to a system file outside the intended directory, potentially leading to information disclosure or unauthorized code execution.

Want to discuss this further? Join the Ameeba Cybersecurity Group Chat.

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.
Ameeba Chat