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...

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-2025-23334

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...

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...

Membership Inference Attack with Partial Features

Machine learning models have been shown to be susceptible to membership inference attack, which can be used to determine whether a given sample appears in the training data. Existing membership inference methods commonly assume that the adversary has full access to the features of the target...

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...

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...

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...

CVE-2025-23335

NVIDIA Triton Inference Server for Windows and Linux and the Tensor RT backend contain a vulnerability where an attacker could cause an underflow by a specific model configuration and a specific input. 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-2025-23324

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-2025-23327

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause an integer overflow through specially crafted inputs. A successful exploit of this vulnerability might lead to denial of service and data tampering...

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-2025-23331

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...

CVE-2025-23326

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

CVE-2025-23321

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a divide by zero issue by issuing an invalid request. A successful exploit of this vulnerability might lead to denial of service...

CVE-2025-23321

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a divide by zero issue by issuing an invalid request. A successful exploit of this vulnerability might lead to denial of service...

CVE-2025-23322

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where multiple requests could cause a double free when a stream is cancelled before it is processed. A successful exploit of this vulnerability might lead to denial of service...