CVE-2025-23331 in Triton Inference Server
Summary
by MITRE • 08/06/2025
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a memory allocation with excessive size value, leading to a segmentation fault, by providing an invalid request. A successful exploit of this vulnerability might lead to denial of service.
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Analysis
by VulDB Data Team • 08/06/2025
The vulnerability identified as CVE-2025-23331 affects NVIDIA Triton Inference Server across both Windows and Linux operating systems, representing a critical memory management flaw that could be exploited to disrupt service availability. This issue stems from insufficient input validation within the server's request processing mechanism, where malformed requests containing excessive size parameters can trigger unintended memory allocation behaviors. The vulnerability specifically manifests when the inference server attempts to process a request with an invalid size value that exceeds acceptable memory boundaries, resulting in a segmentation fault that terminates the service process.
From a technical perspective, this vulnerability operates through a classic buffer overflow pattern where the system allocates memory based on user-provided size parameters without proper bounds checking or validation. The flaw represents a subtype of memory corruption vulnerabilities that fall under CWE-122, which deals with insufficient checking of the size of a buffer, and is closely related to CWE-787, which addresses out-of-bounds write operations. The system's failure to validate input parameters before memory allocation creates a pathway for malicious actors to craft requests that deliberately exceed memory allocation limits, causing the application to crash and potentially exposing the underlying system to further exploitation attempts.
The operational impact of this vulnerability extends beyond simple denial of service, as it can be leveraged by adversaries to disrupt inference services that depend on NVIDIA Triton for machine learning model deployment and execution. In production environments where Triton Inference Server handles critical workloads such as real-time video analysis, autonomous vehicle processing, or financial transaction validation, this vulnerability could result in significant service interruptions and potential business disruption. The segmentation fault that occurs upon exploitation typically results in immediate service termination, requiring manual intervention to restart the inference server and potentially leading to cascading failures in dependent applications that rely on consistent model inference availability.
Security practitioners should implement immediate mitigations including input validation controls, request size limiting mechanisms, and network-level filtering to prevent malformed requests from reaching the inference server. The vulnerability aligns with ATT&CK technique T1499.004, which covers network denial of service attacks, and organizations should consider implementing intrusion detection systems to monitor for suspicious request patterns. Additionally, regular updates and patches should be applied immediately upon release, as this type of memory allocation vulnerability often requires core application modifications to address the root cause. Organizations utilizing Triton Inference Server should also consider implementing redundant service architectures and automated failover mechanisms to minimize the impact of potential exploitation attempts and maintain service availability during remediation activities.