CVE-2026-24259 in TensorRT-LLM
Summary
by MITRE • 07/15/2026
NVIDIA TensorRT-LLM for Linux contains a vulnerability where an attacker could cause missing authentication for a critical function. A successful exploit of this vulnerability might lead to code execution, data tampering, and information disclosure.
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Analysis
by VulDB Data Team • 07/15/2026
This vulnerability in NVIDIA TensorRT-LLM for Linux represents a critical authentication bypass flaw that fundamentally undermines the security posture of the deep learning inference framework. The issue manifests as a missing authentication check for critical functions within the tensor processing pipeline, creating an attack surface where unauthorized parties can exploit the system without proper credentials. This weakness directly violates fundamental security principles and creates pathways for malicious actors to gain elevated privileges and access sensitive computational resources.
The technical implementation flaw stems from inadequate validation mechanisms within the framework's function access controls, particularly in how the system handles authentication tokens and authorization checks for critical tensor processing operations. When the application fails to properly verify user credentials or session legitimacy before executing privileged functions, it creates a persistent vulnerability that can be exploited across multiple attack vectors. This type of flaw typically falls under CWE-285 - Improper Authorization, which specifically addresses situations where systems fail to properly enforce access controls for critical resources.
From an operational perspective, the impact of this vulnerability extends far beyond simple unauthorized access. An attacker who successfully exploits this weakness could execute arbitrary code within the tensor processing environment, potentially leading to complete system compromise. The vulnerability enables data tampering operations that could corrupt neural network models or manipulate inference results, creating both integrity and confidentiality violations. Information disclosure becomes possible as attackers may gain access to sensitive model parameters, training data, or system configurations that should remain protected.
The attack surface for this vulnerability includes scenarios where TensorRT-LLM is deployed in multi-user environments or cloud computing platforms where multiple applications share the same inference infrastructure. Attackers could leverage this weakness to perform lateral movement within neural network processing clusters or to escalate privileges from standard user accounts to administrative access levels. The implications are particularly severe in enterprise environments where these frameworks process sensitive data such as medical imaging, financial analytics, or autonomous vehicle decision-making systems.
Mitigation strategies should include immediate deployment of vendor patches and updates to address the authentication bypass mechanisms. System administrators must implement additional monitoring controls to detect unauthorized access attempts and establish network segmentation to limit exposure of tensor processing services. The implementation of multi-factor authentication for critical functions and regular security audits of access control configurations becomes essential for reducing risk. Organizations should also consider implementing runtime protection mechanisms such as application whitelisting and privilege separation to minimize the potential impact of successful exploitation attempts. This vulnerability aligns with ATT&CK technique T1078 - Valid Accounts, as it exploits legitimate access paths while bypassing authentication controls that should normally prevent unauthorized operations within the system.