Triton Inference Server — Vulnerabilities & Security Advisories 44

This page aggregates known vulnerabilities for NVIDIA Triton Inference Server, categorized by Common Weakness Enumeration (CWE) tags and linked to their respective Common Vulnerabilities and Exposures (CVE) identifiers. It compiles a comprehensive list of security flaws affecting the Triton Inference Server software, covering reported issues from the early days of the project up to the present moment. This aggregation includes critical, high, medium, and low severity findings that impact the stability, confidentiality, and integrity of machine learning model serving environments. By consulting this resource, users can efficiently track NVIDIA’s official advisories and patch releases to maintain a secure deployment posture. Security researchers and system administrators can use this data to gain a deeper understanding of specific weakness classes that frequently arise in inference server architectures, such as buffer overflows, injection flaws, or improper access control mechanisms. Additionally, the page serves as a historical record, allowing stakeholders to look up a product’s vulnerability history over time to assess long-term risk trends and the effectiveness of remediation efforts. This structured view facilitates better decision-making for security auditing, compliance reporting, and prioritizing mitigation strategies across different versions of the software. The content is strictly factual, providing direct links to authoritative sources without interpretive commentary or promotional language.