Preface: AI deployment is accelerated by hardware advancements (especially GPUs), ML platforms and MLOps for automation, the use of pre-trained models via transfer learning, containerization and orchestration for scalability, cloud infrastructure providing on-demand resources, and industry collaborations and specialized data partners to streamline various stages of the AI lifecycle.

Background: NVIDIA Triton Inference Server is an open-source inference serving platform whose primary goal is to simplify and accelerate the deployment of AI models in production environments. It aims to provide a unified platform capable of serving models from various machine learning frameworks, such as TensorFlow, PyTorch, ONNX Runtime, and custom backends, enabling flexibility and interoperability.

The “model name” parameter in NVIDIA Triton Inference Server is a crucial identifier used to specify which model a client wishes to interact with for inference requests.

Client API Usage: When using Triton client libraries (e.g., tritonclient[.]grpc or tritonclient[.]http), the model_name parameter is typically a required argument in functions used to send inference requests.

Both backends (Python and DALI) are part of Triton’s modular architecture. The Python backend often acts as a wrapper or orchestrator for other backends, including DALI.

Vulnerability details:

CVE-2025-23316 NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause a remote code execution by manipulating the model name parameter in the model control APIs. A successful exploit of this vulnerability might lead to remote code execution, denial of service, information disclosure, and data tampering.

CVE-2025-23268 VIDIA Triton Inference Server contains a vulnerability in the DALI backend where an attacker may cause an improper input validation issue. A successful exploit of this vulnerability may lead to code execution.

Official announcement: Please see the link for details –

https://nvidia.custhelp.com/app/answers/detail/a_id/5691

Preface: Debug logs may contain user IDs and passwords to provide diagnostic information for failed login attempts, authentication failures, or to trace user activity within an application, but this is a significant security risk and should be avoided. Security best practices dictate that sensitive information like passwords should never be logged in cleartext. Instead, logging should only include non-sensitive user identifiers to help with troubleshooting without exposing credentials

Background: NVIDIA’s NVDebug tool is part of the broader Nsight Systems tool suite and relies on the NVIDIA Data Center GPU Manager (DCGM) library, specifically utilizing it for data collection and diagnostics to assist in troubleshooting and monitoring NVIDIA GPUs.

• NVDebug is a tool for debugging and profiling NVIDIA GPUs, particularly in data center environments.
• DCGM is a library for managing and monitoring NVIDIA GPUs in clusters and data centers.

NVDebug uses the DCGM library to gather essential diagnostic data, logs, and health information from the GPUs, enabling detailed analysis of the system’s state and performance.

Vulnerability details:

CVE-2025-23342: The NVIDIA NVDebug tool contains a vulnerability that may allow an actor to gain access to a privileged account . A successful exploit of this vulnerability may lead to code execution, denial of service, escalation of privileges, information disclosure and data tampering.

CVE-2025-23343: The NVIDIA NVDebug tool contains a vulnerability that may allow an actor to write files to restricted components. A successful exploit of this vulnerability may lead to information disclosure, denial of service, and data tampering.

Official announcement: Please see the link for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5696

Preface:

• IPMI is a standardized interface for hardware management, operating via the Baseboard Management Controller (BMC).
• It supports both in-band (local) and out-of-band (remote) access.
• BlueField’s reliance on OpenIPMI and IPMItool makes it susceptible to kernel-level vulnerabilities.

Background: The Intelligent Platform Management Interface (IPMI) is a standard interface for hardware management used by system administrators to control the devices and monitor the sensors. For these, it is necessary the IPMI Controller called Baseboard Management Controller (BMC) and a manager software (for example, IPMItool). It provides an interface to manage IPMI functions in a local (in-band) or remote (out-of-band) system.

Vulnerability details:

This advisory explores a potential causal relationship between two recent vulnerabilities:

• CVE-2025-23256 – A high-severity vulnerability in the NVIDIA BlueField DPU management interface, allowing local attackers to bypass authorization and modify configurations.

https://nvidia.custhelp.com/app/answers/detail/a_id/5655

• CVE-2025-38456 – A moderate-severity vulnerability in the Linux IPMI subsystem, involving memory corruption due to mishandled pointers in ipmi_create_user().

https://nvd.nist.gov/vuln/detail/CVE-2025-38456

Recommendations

1. Patch Kernel IPMI Subsystem: Ensure CVE-2025-38456 is mitigated in all systems running BlueField.
2. Update BlueField Firmware: Apply NVIDIA’s latest firmware updates addressing CVE-2025-23256.
3. Audit IPMI Access Controls: Review and restrict local access to /dev/ipmi0 and IPMItool.