Triple Threat in Triton: Critical Flaws Expose AI Servers to Full Takeover

Critical vulnerabilities discovered in the NVIDIA Triton Inference Server platform pose a significant threat to the security of AI infrastructure across both Windows and Linux environments. This concerns an open-source solution designed for large-scale deployment and maintenance of machine learning models—yet, as it now emerges, its Python backend can be exploited to fully compromise a server without authentication.

The team at Wiz reported three vulnerabilities which, when combined strategically, enable remote execution of arbitrary code. The first—CVE-2025-23319, rated 8.1 on the CVSS scale—permits a buffer overflow via a specially crafted request. The second, CVE-2025-23320 (CVSS 7.5), allows an attacker to exceed shared memory limits through oversized input. The third, CVE-2025-23334 (CVSS 5.9), results in out-of-bounds memory reads. While each issue in isolation poses a moderate risk, their combined effect opens a direct path to full server compromise.

At the core lies the mechanism responsible for handling Python-based models, including those built with PyTorch and TensorFlow. The backend permits inference requests via internal IPC (Inter-Process Communication) mechanisms—precisely where these flaws reside.

The attack chain begins with CVE-2025-23320, which allows an adversary to extract the unique name of the shared memory region used for inter-component communication. This identifier, intended to remain concealed, can be retrieved and weaponized as a key. From there, CVE-2025-23319 and CVE-2025-23334 facilitate arbitrary read and write operations within memory, bypassing safeguards. This grants full control over the inference process, opening the door to the injection of malicious payloads, theft or manipulation of AI models, and exfiltration of sensitive data.

Experts warn that a successful Triton compromise could serve as a gateway for broader attacks against an organization’s entire network, including mission-critical infrastructure.

In its August security bulletin, NVIDIA confirmed the existence of these vulnerabilities and strongly urged users to apply the 25.07 update, which addresses them. Additionally, three more serious flaws—CVE-2025-23310, CVE-2025-23311, and CVE-2025-23317—were patched in the same release. These issues could similarly lead to code execution, data leakage, service disruption, and memory corruption.

Though there are no confirmed reports of these vulnerabilities being exploited in the wild, the level of risk and the critical nature of the affected components leave no room for complacency. Organizations relying on Triton are strongly advised to update immediately and reassess their threat models surrounding AI infrastructure.