CVE-2025-23254: NVIDIA TensorRT-LLM Python Executor Data Validation Vulnerability

Overview

A critical vulnerability has been identified in NVIDIA’s TensorRT-LLM platform, specifically affecting its python executor. This vulnerability, designated as CVE-2025-23254, presents a significant risk to businesses and individuals utilizing NVIDIA TensorRT-LLM. The vulnerability enables an attacker with local access to the TRTLLM server to exploit a data validation flaw, leading to potential compromise of the system and potential data leakage.
The severity of this vulnerability cannot be understated. As it may lead to code execution, information disclosure, and data tampering, it poses a serious threat to the integrity of systems and data. Therefore, swift action is needed to mitigate the risks associated with CVE-2025-23254.

Vulnerability Summary

CVE ID: CVE-2025-23254
Severity: Critical (CVSS 8.8)
Attack Vector: Local
Privileges Required: Low
User Interaction: None
Impact: System compromise, information disclosure, and data tampering

Affected Products

Product | Affected Versions

NVIDIA TensorRT-LLM | All versions prior to the patch

How the Exploit Works

The vulnerability lies in the Python executor of NVIDIA’s TensorRT-LLM. An attacker with local access can trigger a data validation issue, causing the system to execute malicious code or disclose sensitive information. This is likely due to a flaw in how the Python executor handles certain types of data or requests, allowing unexpected and potentially harmful input to be processed.

Conceptual Example Code

The following is a conceptual example of how this vulnerability might be exploited:

# Connect to local TRTLLM server
connection = connect_to_trtllm_server()
# Craft malicious payload
malicious_payload = create_malicious_payload()
# Send the payload to the Python executor
response = connection.send_payload_to_python_executor(malicious_payload)
# If the exploit is successful, the response will contain sensitive data or grant control of the system
if exploit_successful(response):
print("Exploit successful!")

In this example, `connect_to_trtllm_server()`, `create_malicious_payload()`, and `exploit_successful()` are placeholders for functions that an attacker might use to connect to the server, craft a malicious payload, and verify the success of the exploit, respectively. This is a hypothetical example and does not represent an actual exploit.

Mitigation and Remediation

Users of NVIDIA’s TensorRT-LLM should immediately apply the vendor’s patch to mitigate the vulnerability. If the patch cannot be applied immediately, using a Web Application Firewall (WAF) or Intrusion Detection System (IDS) can serve as temporary mitigation. However, these are not long-term solutions and the vendor’s patch should be applied as soon as possible.