CVE-2025-23254 in TensorRT-LLM
Summary
by MITRE • 05/01/2025
NVIDIA TensorRT-LLM for any platform contains a vulnerability in python executor where an attacker may cause a data validation issue by local access to the TRTLLM server. A successful exploit of this vulnerability may lead to code execution, information disclosure and data tampering.
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Analysis
by VulDB Data Team • 05/01/2025
The vulnerability identified as CVE-2025-23254 resides within NVIDIA TensorRT-LLM's python executor component, representing a critical security flaw that can be exploited through local access to the TRTLLM server instance. This vulnerability falls under the category of data validation issues, specifically manifesting as a weakness in input sanitization and parameter handling within the server's execution environment. The flaw allows an attacker with local system access to manipulate the validation processes that govern how data is processed and executed within the TensorRT-LLM framework, potentially compromising the entire inference pipeline.
The technical implementation of this vulnerability stems from insufficient validation mechanisms within the python executor that handles model execution requests. When local users interact with the TRTLLM server, they can craft malicious inputs that bypass existing data validation checks, creating opportunities for arbitrary code execution within the server's operational context. This represents a classic path to privilege escalation and system compromise, as the python executor typically operates with elevated permissions to manage GPU resources and execute complex machine learning workloads. The vulnerability's impact extends beyond simple code execution to include information disclosure and data tampering capabilities, making it particularly dangerous in production environments where sensitive model weights, inference data, and system configurations are processed.
From an operational standpoint, this vulnerability poses significant risks to organizations deploying NVIDIA TensorRT-LLM in enterprise or research settings where local access controls may be insufficiently enforced. The local access requirement reduces the attack surface compared to remote exploitation vectors, but it still represents a critical weakness in environments where physical or administrative access to server systems is possible. The implications include potential data breaches through information disclosure, model corruption through data tampering, and complete system compromise via code execution. Organizations utilizing this framework for production inference workloads face heightened risk of intellectual property theft, model poisoning attacks, and service disruption attacks that could affect downstream applications relying on these inference services.
Security mitigations for CVE-2025-23254 should prioritize immediate implementation of access controls and privilege separation measures to prevent unauthorized local access to TRTLLM server instances. System administrators must ensure that only authorized personnel have physical or administrative access to systems running TensorRT-LLM, and that proper least-privilege principles are applied to the python executor processes. The implementation of additional input validation layers and sandboxing mechanisms around the python executor can help contain potential exploitation attempts. Organizations should also consider network segmentation and monitoring solutions to detect unusual access patterns to inference servers. This vulnerability aligns with CWE-20 validation of input and CWE-78 command injection patterns, and from an ATT&CK perspective, it maps to techniques involving privilege escalation and execution through valid accounts, specifically T1068 and T1059. Regular security audits and penetration testing of inference server environments are essential to identify and remediate similar weaknesses in machine learning deployment frameworks.