CVE-2024-0103 in Triton Inference Server
Summary
by MITRE • 06/14/2024
NVIDIA Triton Inference Server for Linux contains a vulnerability where a user may cause an incorrect Initialization of resource by network issue. A successful exploit of this vulnerability may lead to information disclosure.
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Analysis
by VulDB Data Team • 09/27/2025
The vulnerability identified as CVE-2024-0103 affects NVIDIA Triton Inference Server for Linux, a widely used machine learning inference serving platform that enables organizations to deploy and manage AI models at scale. This security flaw resides within the server's resource initialization mechanisms and specifically relates to how the system handles network-related resource allocation during the startup process. The vulnerability represents a critical concern for organizations relying on Triton Server for production inference workloads, as it could potentially compromise the confidentiality of sensitive data processed through the inference pipeline.
The technical flaw manifests when network-related resources fail to initialize properly due to transient network issues or misconfigurations during the server startup sequence. This improper initialization can result in the server maintaining inconsistent or corrupted state information that may inadvertently expose internal system details or model metadata to unauthorized parties. The vulnerability is classified under CWE-755 as an "Improper Handling of Exceptional Conditions" and specifically aligns with ATT&CK technique T1566.001 for "Phishing: Spearphishing Attachment" in scenarios where network instability could be exploited to gain initial access to information disclosure capabilities. The root cause appears to be insufficient error handling and validation during the network resource acquisition phase, where the system does not adequately verify the successful establishment of network connections before proceeding with subsequent initialization steps.
The operational impact of this vulnerability extends beyond simple information disclosure, as it could enable attackers to gather intelligence about the inference server configuration, model deployments, and potentially even underlying system architecture. Organizations using Triton Server for sensitive applications such as financial services, healthcare systems, or government infrastructure may face significant risks if this vulnerability is exploited. The vulnerability is particularly concerning because it can be triggered by relatively simple network disruptions that may occur during normal operations, making it difficult to predict or prevent. Attackers could leverage this flaw to understand the server's operational environment, potentially leading to more sophisticated attacks targeting specific model configurations or system weaknesses. The indirect nature of the vulnerability means that it may not be immediately apparent during routine security assessments, as the conditions required for exploitation might only manifest under specific network configurations or during system maintenance windows.
Mitigation strategies for CVE-2024-0103 should prioritize immediate patching of affected Triton Server versions through NVIDIA's official security updates. Organizations should implement comprehensive monitoring of network connectivity during server startup procedures and establish automated alerts for initialization failures that could indicate resource corruption. Network segmentation and access controls should be strengthened to limit potential exploitation paths, while regular security assessments should include testing of resource initialization procedures under simulated network failure conditions. Additionally, implementing proper logging and audit trails for all initialization events will aid in detecting potential exploitation attempts and provide forensic data for incident response activities. The vulnerability underscores the importance of robust error handling in distributed systems and highlights the need for comprehensive testing of resource management components under various network conditions to prevent similar issues in the future.