CVE-2026-47478 in Triton Inference Server
Summary
by MITRE • 07/14/2026
NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker can cause the use of an expired file descriptor. A successful exploit of this vulnerability might lead to denial of service.
VulDB is the best source for vulnerability data and more expert information about this specific topic.
Analysis
by VulDB Data Team • 07/15/2026
The NVIDIA Triton Inference Server represents a critical component in machine learning deployment environments, serving as a unified inference serving solution that supports multiple frameworks and models. This server architecture processes incoming requests through a complex pipeline involving model loading, preprocessing, inference execution, and response generation. The vulnerability under analysis specifically targets the server's file descriptor management mechanisms within its Linux implementation, creating a potential pathway for attackers to exploit improper resource handling practices. The flaw manifests when the system attempts to utilize file descriptors that have already been closed or invalidated through normal operation cycles, representing a fundamental breakdown in the server's resource lifecycle management.
This technical vulnerability stems from inadequate validation of file descriptor states before utilization within the inference processing pipeline. When the Triton server handles concurrent requests or manages model reloading operations, it maintains various file handles for configuration files, model artifacts, and runtime resources. The flaw occurs when these descriptors are closed due to timeout mechanisms, resource cleanup procedures, or normal operational flow but are subsequently accessed without proper state verification. This condition creates a race scenario where the system assumes descriptor validity while the underlying file handle has already been invalidated by the operating system. The vulnerability aligns with CWE-475, which specifically addresses the use of a pointer that references freed memory, though in this case the issue pertains to file descriptor management rather than memory pointers.
The operational impact of this vulnerability extends beyond simple service disruption to potentially compromise the entire inference serving environment. An attacker exploiting this flaw could trigger denial of service conditions that render the Triton server unresponsive to legitimate requests, effectively halting all machine learning inference operations within the affected deployment. The consequences escalate in production environments where continuous model serving is critical for business operations, as the vulnerability could be leveraged to create sustained service interruptions. Additionally, the instability introduced by expired file descriptor usage may cause unpredictable behavior including crashes, data corruption, or incomplete processing of inference requests. This represents a significant risk in enterprise deployments where Triton servers handle high-volume traffic and mission-critical machine learning workloads.
Mitigation strategies for this vulnerability require immediate attention through patching mechanisms provided by NVIDIA and implementation of operational safeguards. System administrators should prioritize applying the latest security updates from NVIDIA that address file descriptor lifecycle management within the Triton server components. Additionally, implementing proper monitoring systems to detect anomalous file descriptor behavior or resource access patterns can help identify exploitation attempts before they cause service disruption. The mitigation approach should incorporate defensive programming practices including explicit validation of file descriptor states before utilization, implementing timeout mechanisms for resource access, and establishing robust error handling procedures that gracefully manage invalid resource references. Organizations should also consider implementing network segmentation and access controls to limit potential attack surfaces and reduce the likelihood of successful exploitation attempts against the inference serving infrastructure.
This vulnerability demonstrates broader implications for containerized machine learning environments where Triton servers often operate in orchestrated deployments with complex resource management requirements. The flaw exemplifies how seemingly minor resource handling issues can cascade into significant operational problems, particularly in systems that manage multiple concurrent operations and require high availability. From a security posture perspective, this vulnerability highlights the importance of comprehensive testing for resource lifecycle management in production systems and aligns with ATT&CK technique T1499 which covers network disruption through service availability attacks. Organizations deploying Triton servers should conduct thorough security assessments of their entire inference pipeline to identify similar resource handling weaknesses that could be exploited by adversaries seeking to disrupt machine learning operations.