CVE-2026-47479 in Triton Inference Server
Summary
by MITRE • 07/14/2026
NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker can cause uncontrolled resource consumption. A successful exploit of this vulnerability might lead to denial of service.
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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 facilitates efficient model serving across diverse hardware configurations including CPUs, GPUs, and specialized accelerators. The vulnerability under examination manifests as an uncontrolled resource consumption issue that fundamentally compromises the server's operational integrity and availability. When exploited, this weakness allows adversaries to exhaust system resources such as memory, CPU cycles, or network bandwidth through carefully crafted requests or model loading operations.
The technical flaw resides in the server's resource management mechanisms during inference request processing and model lifecycle operations. Attackers can construct malicious requests that trigger excessive memory allocation or thread creation within the Triton server processes. This typically occurs when the server fails to properly validate input parameters, model configurations, or request payloads before initiating resource-intensive operations. The vulnerability may also stem from inadequate rate limiting, insufficient resource quotas, or missing bounds checking in the server's internal processing pipelines. Such weaknesses enable attackers to perform resource exhaustion attacks through repeated requests or by submitting malformed inputs that cause the system to allocate resources without proper cleanup or termination.
The operational impact of this vulnerability extends beyond simple service disruption to potentially compromise entire ML inference pipelines and associated applications. A successful denial of service attack can render the inference server unavailable to legitimate users, causing cascading failures in dependent systems that rely on timely model predictions. In production environments where Triton servers handle high volumes of inference requests, this vulnerability could result in significant business disruption, revenue loss, and compromised customer experience. The resource exhaustion may also affect other services running on the same host system, leading to broader operational degradation.
Mitigation strategies for this vulnerability should encompass multiple layers of defense including input validation, resource limiting, and monitoring mechanisms. Organizations should implement strict request rate limiting and connection pooling controls to prevent abuse of server resources. Configuration parameters should be adjusted to enforce maximum memory usage limits and thread count restrictions within the Triton server. Regular security updates and patches from NVIDIA should be applied promptly to address known vulnerabilities. Network segmentation and access controls can limit exposure to unauthorized users while implementing comprehensive monitoring solutions to detect anomalous resource consumption patterns. Additionally, organizations should conduct regular vulnerability assessments and penetration testing to identify potential attack vectors and ensure proper configuration of the inference server environment.
This vulnerability aligns with CWE-400 which describes "Uncontrolled Resource Consumption" as a fundamental weakness in software design where applications fail to properly manage system resources. The attack surface maps to several ATT&CK techniques including privilege escalation through resource exhaustion, denial of service attacks, and system compromise via service disruption. Proper implementation of resource quotas, input sanitization, and access controls directly addresses the underlying causes identified in both CWE and ATT&CK frameworks. Organizations should also consider implementing automated alerting systems that can detect unusual resource consumption patterns and trigger immediate response protocols to prevent exploitation from escalating into more severe security incidents.