CVE-2024-0100 in Triton Inference Server
Summary
by MITRE • 05/14/2024
NVIDIA Triton Inference Server for Linux contains a vulnerability in the tracing API, where a user can corrupt system files. A successful exploit of this vulnerability might lead to denial of service and data tampering.
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Analysis
by VulDB Data Team • 09/19/2025
The NVIDIA Triton Inference Server represents a critical component in machine learning deployment environments, serving as a flexible inference serving solution that enables organizations to deploy and manage AI models at scale. This server software operates within Linux environments and provides various APIs for model management, inference execution, and system monitoring. The tracing API functionality within this server is designed to provide detailed operational insights and performance monitoring capabilities for deployed machine learning models. However, the vulnerability identified in CVE-2024-0100 specifically targets this tracing API component, creating a significant security risk that could compromise the integrity and availability of the entire inference serving infrastructure.
The technical flaw within the tracing API stems from inadequate input validation and sanitization mechanisms that fail to properly handle malformed or maliciously crafted requests. When a user submits crafted requests to the tracing API endpoint, the system does not sufficiently validate the incoming data structures or parameters, allowing for buffer overflows, path traversal attacks, or other file system manipulation techniques. This vulnerability manifests as a result of insufficient bounds checking and improper handling of user-supplied data within the API processing pipeline, creating opportunities for attackers to manipulate file system operations through the tracing interface. The weakness aligns with common software security vulnerabilities such as those classified under CWE-121, which addresses buffer overflow conditions, and CWE-22, which covers path traversal issues.
The operational impact of this vulnerability extends beyond simple denial of service conditions to encompass potential data tampering scenarios that could severely compromise machine learning workflows and model integrity. Successful exploitation could allow attackers to corrupt critical system files, modify model artifacts, or disrupt the inference serving process, leading to service degradation or complete system unavailability. Organizations relying on NVIDIA Triton Inference Server for production workloads face significant risks including model corruption, inference result manipulation, and potential data loss that could affect downstream applications and decision-making processes. The vulnerability particularly threatens environments where the inference server operates with elevated privileges or where model artifacts are stored in shared or accessible file system locations.
Security mitigations for this vulnerability should focus on immediate patch application from NVIDIA, which typically involves updating the Triton Inference Server software to a version that includes proper input validation and sanitization mechanisms. Network segmentation and access control measures should be implemented to limit exposure of the tracing API to trusted users only, while also considering the principle of least privilege for API access. Organizations should also implement monitoring solutions that can detect anomalous API access patterns or unusual file system modifications that might indicate exploitation attempts. From an ATT&CK framework perspective, this vulnerability maps to techniques such as T1059.001 for command and script interpreter and T1486 for data manipulation, highlighting the need for comprehensive defensive measures including API gateway security controls and robust logging mechanisms for security monitoring. Regular security assessments and penetration testing should be conducted to identify potential exploitation vectors and ensure that all security controls remain effective against evolving threats.