CVE-2026-47625 in Triton Inference Server
Summary
by MITRE • 09/08/2026
NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker could abuse missing authorization. A successful exploit of this vulnerability might lead to information disclosure, data tampering, and denial of service.
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Analysis
by VulDB Data Team • 09/08/2026
The NVIDIA Triton Inference Server is a critical component in modern machine learning infrastructure, designed to streamline the deployment and serving of deep learning models across various hardware platforms including GPUs, CPUs, and TPUs. As organizations increasingly rely on these servers for real-time inference at scale, the security posture of the underlying software becomes paramount. The identified vulnerability centers on missing authorization controls within specific endpoints or functionalities of the Triton Inference Server architecture. This flaw indicates that the server fails to adequately verify whether a requesting entity possesses the necessary permissions before executing sensitive operations. Such an oversight is particularly dangerous in multi-tenant environments where multiple users or services may interact with the same inference endpoint, as it allows unauthorized actors to bypass access restrictions entirely.
From a technical perspective, this vulnerability represents a classic case of broken access control, often categorized under CWE-284 Improper Access Control. The absence of robust authentication and authorization checks means that any client capable of reaching the server network interface can potentially interact with protected resources without valid credentials or privileges. This lack of enforcement allows an attacker to perform actions such as retrieving sensitive model artifacts, modifying inference parameters, or accessing internal system states that should remain isolated from external requests. The severity is amplified by the fact that Triton servers are often exposed directly to application front-ends or public networks in production environments, increasing the attack surface significantly compared to internally restricted services.
The operational impact of exploiting this vulnerability is severe and multifaceted. First, information disclosure can occur if an attacker accesses model metadata, configuration files, or even raw input data processed by the server, potentially revealing proprietary algorithms or sensitive customer data embedded in inference requests. Second, data tampering becomes possible when an unauthorized user alters request parameters to manipulate output results, leading to incorrect decisions in downstream applications that rely on these predictions for critical operations such as medical diagnosis or financial trading. Finally, denial of service can be achieved by sending malformed or resource-intensive requests that exhaust server memory or CPU cycles, effectively rendering the inference service unavailable to legitimate users and disrupting business continuity.
This vulnerability aligns with several tactics described in the MITRE ATT&CK framework for enterprise security. Specifically, it relates to Initial Access techniques where attackers exploit insufficient authentication mechanisms to gain entry into a system. It also intersects with Discovery methods if the attacker uses the lack of authorization to enumerate available models and endpoints, and potentially Execution or Impact categories if they leverage the access to deploy malicious payloads or disrupt services. Understanding these mappings helps security teams contextualize the risk within broader threat landscapes and prioritize remediation efforts based on potential adversary behaviors rather than just technical severity scores.
To mitigate this vulnerability, immediate action is required to enforce strict authorization policies across all Triton Inference Server endpoints. Administrators should implement robust authentication mechanisms such as OAuth2 or JWT-based token validation for every incoming request. Role-Based Access Control (RBAC) should be configured to ensure that users and services only have access to the specific models and operations necessary for their function, adhering to the principle of least privilege. Additionally, deploying a reverse proxy with WAF capabilities can provide an additional layer of defense by filtering out unauthorized requests before they reach the Triton server. Regular security audits and penetration testing should be conducted to identify similar gaps in access control logic across other components of the machine learning pipeline. Keeping the NVIDIA Triton Inference Server updated to the latest version is also essential, as vendors frequently release patches that address such critical authorization flaws discovered during internal reviews or community reporting.