Recent research by Wiz Security has identified a significant chain of vulnerabilities within NVIDIA’s Triton Inference Server, a widely used platform for deploying AI models at scale. When exploited in sequence, these flaws could enable unauthenticated attackers to gain full control over affected servers, leading to remote code execution (RCE).
The vulnerabilities, assigned CVE (Common Vulnerabilities & Exposure) IDs CVE-2025-23319, CVE-2025-23320, and CVE-2025-23334, stem from issues in Triton’s Python backend, which is responsible for executing models written in Python and serving as a dependency for other backends. The core of the issue involves improper handling of internal shared memory regions used for high-performance communication between processes.
The attack chain involves three main steps:
Information Disclosure
An attacker can trigger a crafted request that results in Triton revealing internal shared memory identifiers through error messages. This unintended information leak exposes internal IPC mechanisms.
Abuse of Shared Memory API
Using the leaked shared memory identifiers, an attacker can manipulate Triton’s API to register internal shared memory regions as if they were legitimate user-provided regions. Since the system does not validate these keys, this allows unauthorized access to internal memory.
Potential for Remote Code Execution
With control over internal shared memory, an attacker can corrupt data structures or craft malicious IPC messages. This can lead to memory corruption or logical exploits, ultimately enabling remote code execution on the server.
Successful exploitation could allow an attacker to potentially steal proprietary AI models stored on the server, access or manipulate sensitive data processed by AI models, alter model outputs, leading to misinformation or operational issues, or use the compromised server as a foothold for broader network attacks
NVIDIA has released patches addressing these issues in version 25.07 and later. It is crucial for organizations using Triton to immediately update affected instances to the latest version, limit network exposure of Triton servers and enforce strict access controls, and monitor runtime behavior for signs of suspicious activity.
This discovery highlights the importance of comprehensive security practices in deploying AI systems. Regular updates, vulnerability assessments, and strict access controls are essential to safeguard against evolving attack vectors.
For further details and the full analysis, see Wiz’s official research report here.
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