NVIDIA Triton Server Exposes Critical Security Vulnerabilities
▼ Summary
– Researchers discovered critical vulnerabilities in NVIDIA’s Triton Inference Server, allowing remote attackers to gain full control and execute remote code (RCE).
– The vulnerabilities (CVE-2025-23319, CVE-2025-23320, CVE-2025-23334) could lead to model theft, data breaches, response manipulation, and network pivoting.
– NVIDIA acknowledged the flaws on May 16 and released a patch on August 4, urging users to update immediately.
– The exploit chain starts with a flaw in the Python backend’s error handling, leaking internal memory details, which attackers can misuse via API calls.
– This marks the latest in a series of NVIDIA vulnerabilities uncovered by Wiz Research, including prior container escape flaws.
Security researchers have uncovered a series of high-risk vulnerabilities in NVIDIA’s Triton Inference Server, posing serious threats to organizations deploying AI models at scale. The flaws, identified shortly after another NVIDIA Container Toolkit vulnerability emerged, could enable attackers to take full control of affected systems remotely without authentication.
The Triton Inference Server, widely used for deploying machine learning models across frameworks like TensorFlow and PyTorch, was found to have weaknesses that could lead to remote code execution (RCE). NVIDIA has classified these vulnerabilities under CVE-2025-23319, CVE-2025-23320, and CVE-2025-23334, warning of severe consequences if exploited.
A successful attack could result in:
- Model theft, allowing cybercriminals to steal proprietary AI models
- Data breaches, exposing sensitive information processed by AI systems
- Response manipulation, where attackers alter model outputs to deliver misleading or harmful results
- Network pivoting, using compromised servers to infiltrate broader organizational infrastructure
Wiz Research, the team behind the discovery, reported the issues to NVIDIA on May 15, with the company acknowledging them the following day. Patches were rolled out on August 4 via an official security bulletin, urging users to update immediately.
How the Exploit Works The vulnerabilities primarily target Triton’s Python backend, a widely adopted component for AI inference. Researchers found that improper error handling could leak critical internal details, including the name of a shared memory region used for inter-process communication (IPC).
For example, an error message might inadvertently expose sensitive identifiers like: `{“error”:”Failed to increase the shared memory pool size for key ‘tritonpythonbackendshmregion_4f50c226-b3d0-46e8-ac59-d4690b28b859’…”}`
Armed with this information, attackers can manipulate Triton’s public API to gain unauthorized access. By registering the leaked memory key, they can craft malicious inference requests, effectively hijacking the server’s private memory space. This grants them read/write capabilities, enabling further exploitation, including full system takeover.
This marks the latest in a string of NVIDIA-related vulnerabilities uncovered by Wiz, following earlier discoveries like CVE-2025-23266 and CVE-2024-0132, which involved container escape techniques.
Organizations relying on Triton for AI workloads should prioritize applying the latest patches to mitigate these risks.
(Source: InfoSecurity Magazine)