CVE-2025-23254: NVIDIA TensorRT-LLM RCE Vulnerability

CVE-2025-23254 Overview

NVIDIA TensorRT-LLM for any platform contains a vulnerability in the python executor where an attacker may cause a data validation issue by local access to the TRTLLM server. This vulnerability arises from insecure deserialization (CWE-502) in the Python executor component. A successful exploit of this vulnerability may lead to code execution, information disclosure, and data tampering.

Critical Impact

Local attackers with access to the TRTLLM server can exploit this insecure deserialization vulnerability to execute arbitrary code, disclose sensitive information, and tamper with data, potentially compromising AI/ML infrastructure.

Affected Products

• NVIDIA TensorRT-LLM (all platforms)
• NVIDIA TensorRT-LLM Python Executor component
• Systems running TRTLLM server with local access

Discovery Timeline

• 2025-05-01 - CVE-2025-23254 published to NVD
• 2025-05-02 - Last updated in NVD database

Technical Details for CVE-2025-23254

Vulnerability Analysis

This vulnerability is classified as an insecure deserialization flaw (CWE-502) affecting the Python executor component of NVIDIA TensorRT-LLM. Insecure deserialization vulnerabilities occur when an application deserializes untrusted data without proper validation, allowing attackers to manipulate serialized objects to achieve malicious outcomes.

In the context of TensorRT-LLM, the Python executor processes serialized data that, when improperly validated, can be exploited by an attacker with local access to the TRTLLM server. The vulnerability requires local access to the target system, meaning the attacker must already have some level of access to the infrastructure running the TensorRT-LLM service.

The scope of this vulnerability is changed, meaning a successful exploit can affect resources beyond the vulnerable component's security authority. This makes the vulnerability particularly concerning in shared or multi-tenant AI/ML environments where compromising one component could lead to broader infrastructure compromise.

Root Cause

The root cause of this vulnerability is insufficient data validation in the Python executor's deserialization routines. When the TensorRT-LLM server processes serialized Python objects, it fails to adequately verify the integrity and safety of the incoming data before reconstructing objects in memory. This lack of validation allows malicious serialized payloads to be processed, potentially instantiating dangerous objects or executing arbitrary code during the deserialization process.

Attack Vector

The attack vector requires local access to the TRTLLM server. An attacker with legitimate or illicit local access to the system running TensorRT-LLM can craft malicious serialized payloads targeting the Python executor component. When these payloads are processed by the vulnerable deserialization routine, the attacker can achieve code execution within the context of the TensorRT-LLM process.

The exploitation flow typically involves:

1. Gaining local access to the system running TRTLLM server
2. Crafting a malicious serialized Python object designed to execute arbitrary commands
3. Submitting the payload to the Python executor component
4. The vulnerable deserialization routine processes the malicious payload
5. Arbitrary code executes with the privileges of the TensorRT-LLM process

For technical details on this vulnerability, refer to the NVIDIA Security Response.

Detection Methods for CVE-2025-23254

Indicators of Compromise

• Unusual process spawning from TensorRT-LLM or related Python processes
• Unexpected network connections originating from the TRTLLM server process
• Anomalous file system activity in directories associated with TensorRT-LLM
• Suspicious serialized data being passed to the Python executor component

Detection Strategies

• Monitor for abnormal Python process behavior including unexpected child processes or system calls
• Implement file integrity monitoring on TensorRT-LLM installation directories
• Configure logging to capture all input processing by the Python executor component
• Deploy behavioral analysis to detect serialized payload injection attempts

Monitoring Recommendations

• Enable comprehensive logging for all TensorRT-LLM components, particularly the Python executor
• Monitor local user activity on systems running TRTLLM servers for suspicious patterns
• Implement process monitoring to detect code execution attempts from TensorRT-LLM processes
• Establish baseline behavior for TensorRT-LLM operations to identify anomalies

How to Mitigate CVE-2025-23254

Immediate Actions Required

• Review and restrict local access to systems running TRTLLM servers to authorized personnel only
• Implement strict access controls and authentication for local system access
• Audit current users with local access to TensorRT-LLM infrastructure
• Monitor the NVIDIA Security Response for official patch availability

Patch Information

NVIDIA has published a security advisory addressing this vulnerability. Administrators should consult the NVIDIA Support Response for the latest patch information and update instructions. Apply vendor-provided patches as soon as they become available to remediate this vulnerability.

Workarounds

• Restrict local access to TRTLLM servers using principle of least privilege
• Implement network segmentation to isolate AI/ML infrastructure from general-purpose systems
• Enable enhanced logging and monitoring on affected systems until patches can be applied
• Consider running TensorRT-LLM in containerized environments with strict resource isolation

# Example: Restrict file permissions on TensorRT-LLM installation
chmod -R 750 /path/to/tensorrt-llm/
chown -R root:tensorrt-admins /path/to/tensorrt-llm/

# Example: Limit local user access to the TRTLLM server
usermod -aG tensorrt-admins authorized_user