CVE-2026-47476
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 • 07/14/2026
The NVIDIA Triton Inference Server represents a critical component in machine learning deployment environments, serving as a unified inference serving solution that enables organizations to deploy and manage multiple AI models across various hardware configurations. This server software operates as a centralized platform for handling inference requests from client applications, making it a prime target for adversaries seeking to disrupt service availability. The vulnerability under examination manifests as an uncontrolled resource consumption issue that fundamentally compromises the server's ability to maintain normal operations and respond to legitimate requests.
This specific weakness stems from inadequate resource management mechanisms within the Triton Inference Server implementation, allowing malicious actors to craft specially formatted requests or exploit specific processing paths that cause excessive CPU utilization, memory allocation, or I/O operations. The flaw typically occurs when the server fails to properly validate request parameters or limit concurrent resource usage, enabling attackers to consume system resources at rates that exceed normal operational thresholds. Such behavior creates a condition where legitimate inference requests cannot be processed due to resource exhaustion, effectively rendering the service unavailable to authorized users while maintaining the server's operational status.
The operational impact of this vulnerability extends beyond simple service disruption, as it can cascade into broader system instability and potential data loss scenarios. When resource consumption spirals out of control, the affected inference server may experience complete denial of service conditions that can persist for extended periods until manual intervention occurs or system resources are restored through restart operations. Organizations relying on Triton Inference Server for critical AI workloads face significant business risks including production downtime, revenue loss, and potential compromise of downstream applications that depend on timely model inference responses.
From a cybersecurity perspective, this vulnerability aligns with CWE-400, which categorizes improper resource management as a fundamental weakness in software design. The attack surface is particularly concerning given that the exploit can be executed through standard network communication channels without requiring privileged access or specialized tools, making it accessible to threat actors of varying skill levels. This vulnerability also maps to ATT&CK technique T1499.004, which covers network denial of service attacks through resource consumption, demonstrating how attackers can leverage this flaw to systematically degrade service availability.
Mitigation strategies should encompass both immediate defensive measures and long-term architectural improvements. Organizations must implement request rate limiting and resource quotas to prevent any single client or process from consuming disproportionate system resources. Network-level controls including firewall rules and intrusion detection systems can help identify and block anomalous traffic patterns that indicate exploitation attempts. Additionally, regular software updates and patches should be prioritized to address known vulnerabilities in the Triton Inference Server implementation. Monitoring solutions that track resource utilization metrics and alert on unusual consumption patterns provide early warning capabilities that enable rapid response to potential exploitation attempts before they can cause significant service disruption.