NVIDIA Triton Inference Server Code Execution Vulnerability (CNVD-2025-20011)

NVIDIA Triton Inference Server is an open source software from NVIDIA that helps standardize model deployment and deliver fast and scalable AI in production. A code execution vulnerability exists in NVIDIA Triton Inference Server, which can be exploited by attackers to execute arbitrary code, cau...

NVIDIA Triton Inference Server Integer Overflow Vulnerability

Triton Inference Server is a high-performance inference service engine developed by NVIDIA, designed for AI model deployment in production environments, with support for a variety of frameworks TensorFlow, PyTorch, ONNX, etc. and optimized inference performance for GPUs and CPUs. An integer...

NVIDIA Triton Inference Server Code Execution Vulnerability

NVIDIA Triton Inference Server is an open source software from NVIDIA that helps standardize model deployment and deliver fast and scalable AI in production. A code execution vulnerability exists in NVIDIA Triton Inference Server, which can be exploited by attackers to execute arbitrary code, cau...

NVIDIA Triton Inference Server Denial of Service Vulnerability (CNVD-2025-20009)

NVIDIA Triton Inference Server is an open source software from NVIDIA that helps standardize model deployment and deliver fast and scalable AI in production. A denial of service vulnerability exists in NVIDIA Triton Inference Server, which stems from an invalid request that could result in a...

NVIDIA Triton Inference Server Stack Overflow Vulnerability

Triton Inference Server is a high-performance inference service engine developed by NVIDIA, designed for AI model deployment in production environments, with support for a variety of frameworks TensorFlow, PyTorch, ONNX, etc. and optimized inference performance for GPUs and CPUs. A stack overflow...

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) relates to integer overflow. This vulnerability allows a malicious actor to trigger a service failure.

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server previously known as TensorRT Inference Server is related to a numerical overflow issue. Exploiting this vulnerability could allow an attacker to cause service failures...

The vulnerability of the software for deploying and executing AI models developed with NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) allows a malicious actor to trigger a service failure. This vulnerability is related to zero-division errors.

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server formerly TensorRT Inference Server is related to division by zero errors. Exploiting this vulnerability can allow a malicious actor to trigger a service failure by sending a specially crafted request...

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) relates to integer overflow. This vulnerability allows a malicious actor to trigger a service failure.

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server previously known as TensorRT Inference Server is related to a numerical overflow issue. Exploiting this vulnerability could allow an attacker to cause service failures...

Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-Cache in LLM Inference

The Key-Value KV cache, which stores intermediate attention computations Key and Value pairs to avoid redundant calculations, is a fundamental mechanism for accelerating Large Language Model LLM inference. However, this efficiency optimization introduces significant yet underexplored privacy risk...

The vulnerability of the software for deploying and executing AI models with NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) lies in its ability to allow numerical overflow, which can lead to service interruptions for attackers.

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server previously known as TensorRT Inference Server is related to the possibility of numerical overflow. Exploiting this vulnerability could allow a malicious actor to cause service failures...

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) relates to integer overflow. This vulnerability allows a malicious actor to trigger a service failure.

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server previously known as TensorRT Inference Server is related to a numerical overflow issue. Exploiting this vulnerability could allow an attacker to cause service failures...

The vulnerability of the software for deploying and executing AI models developed by NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) relates to a memory reclamation error. This vulnerability allows attackers to trigger a service failure.

The vulnerability of the NVIDIA Triton Inference Server previously known as TensorRT Inference Server software for deploying and executing AI models is related to a memory reclamation error. Exploiting this vulnerability could allow a remote attacker to cause a service failure...

The vulnerability of the software for deploying and executing AI models developed by NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) relates to buffer overflows in dynamic memory. This vulnerability allows attackers to cause system failures, gain unauthorized access to protected information, and execute arbitrary code.

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server previously known as TensorRT Inference Server is related to buffer overflow in dynamic memory. Exploiting this vulnerability can allow a remote attacker to cause service failures, gain unauthorized access...

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server (previously known as TensorRT Inference Server) relates to integer overflow. This vulnerability allows a malicious actor to trigger a service failure.

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server previously known as TensorRT Inference Server is related to a numerical overflow issue. Exploiting this vulnerability could allow an attacker to cause service failures...

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server (formerly TensorRT Inference Server) lies in its uncontrolled recursion. This allows a malicious actor to trigger a service failure.

The vulnerability of the software for deploying and executing NVIDIA Triton Inference Server previously known as TensorRT Inference Server is related to uncontrolled recursion. Exploiting this vulnerability could allow a malicious actor to cause service failures...

NVIDIA Triton Detected

This is an informational plugin to inform the user that the scanner has detected a publicly accessible NVIDIA Triton instance on the target application. NVIDIA Triton provides an optimized cloud and edge inferencing solution. This detection is included in the AI and LLM category. No source data...

CVE-2025-23324

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause an integer overflow or wraparound, leading to a segmentation fault, by providing an invalid request. A successful exploit of this vulnerability might lead to denial of service...

CVE-2025-23325

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause uncontrolled recursion through a specially crafted input. A successful exploit of this vulnerability might lead to denial of service...

CVE-2025-23334

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by sending a request. A successful exploit of this vulnerability might lead to information disclosure...

Label Inference Attacks against Federated Unlearning

Federated Unlearning FU has emerged as a promising solution to respond to the right to be forgotten of clients, by allowing clients to erase their data from global models without compromising model performance. Unfortunately, researchers find that the parameter variations of models induced by FU...