CVE-2025-33202 identifies a stack-based buffer overflow vulnerability in NVIDIA Triton Inference Server, a widely used AI model serving platform deployed on Linux and Windows. The flaw arises when the server processes excessively large payloads sent by an attacker, leading to a stack overflow condition. This vulnerability is classified under CWE-121, indicating improper handling of buffer boundaries on the stack. Exploitation requires network access and low privileges, but no user interaction is necessary. Successful exploitation results in denial of service by crashing or destabilizing the server process, potentially interrupting AI inference workloads. The vulnerability affects all Triton Inference Server versions prior to 25.09, with no patches currently published. The CVSS 3.1 base score of 6.5 reflects a medium severity, driven by network attack vector, low attack complexity, and the impact limited to availability. No confidentiality or integrity impacts are reported. No known exploits have been observed in the wild, but the vulnerability poses a risk to organizations relying on Triton for AI inference services, especially in environments where uptime and reliability are critical. The technical details confirm the vulnerability was reserved in April 2025 and published in November 2025, with NVIDIA as the assigner.

For European organizations, the primary impact is service disruption due to denial of service on AI inference workloads powered by NVIDIA Triton Inference Server. This can affect sectors relying on AI-driven applications such as autonomous vehicles, healthcare diagnostics, financial services, and manufacturing automation. Interruptions could lead to operational delays, degraded service quality, and potential financial losses. Since the vulnerability does not compromise data confidentiality or integrity, direct data breaches are unlikely. However, availability impacts can cascade, affecting dependent systems and business processes. Organizations with large-scale AI deployments or those providing AI-as-a-service are at higher risk. The medium severity rating suggests moderate urgency but should not be underestimated given the critical role of AI inference in digital transformation initiatives across Europe.

1. Apply the official patch or upgrade to NVIDIA Triton Inference Server version 25.09 or later as soon as it becomes available. 2. Until patches are deployed, restrict network access to the Triton server using firewalls or network segmentation to limit exposure to trusted users and systems only. 3. Implement input validation and payload size restrictions at the network perimeter or application gateway to block abnormally large requests. 4. Monitor server logs and network traffic for unusual payload sizes or repeated malformed requests that could indicate exploitation attempts. 5. Employ runtime protections such as stack canaries or address space layout randomization (ASLR) if supported by the deployment environment to mitigate buffer overflow impacts. 6. Conduct regular security assessments and penetration testing focused on AI infrastructure to identify and remediate similar vulnerabilities proactively. 7. Establish incident response plans specific to AI service disruptions to minimize downtime in case of exploitation.