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VulnerabilityPublished

CVE-2025-23316: NVIDIA Triton Inference Server · OS Command Injection

Affects NVIDIA Triton Inference ServerSource CVE Database V5

0

Not known to be exploited · CVSS 9.8 Critical : upgrade to 25.08 as a priority.

Data as of 26 Oct 2026, 10:12 UTC

CVSS 3.1 base score
9.8Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.7% chance of exploitation in 30 days – higher than 53% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 25.08
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 17 Sep 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

What to do

Upgrade NVIDIA Triton Inference Server to 25.08.

Affected software

ProductAffectedFixed in
NVIDIA Triton Inference ServerAffected
  • <25.08
Fixed in
  • 25.08

AI-suggested

Immediate mitigation involves upgrading the NVIDIA Triton Inference Server to version 25.08 or later, where the vulnerability has been addressed. Organizations should prioritize patching exposed servers, especially those accessible from untrusted networks. In the interim, restricting network access to the model control APIs through firewall rules or network segmentation can reduce exposure. Implementing strict input validation and sanitization on the model name parameter at the application layer can serve as an additional safeguard if patching is delayed. Monitoring and logging access to the model control APIs should be enhanced to detect anomalous requests indicative of exploitation attempts. Employing host-based intrusion detection systems (HIDS) to monitor for suspicious command executions can also help in early detection. Finally, conducting a thorough audit of AI infrastructure to identify all instances of Triton Inference Server and ensuring they are updated or isolated is critical. Organizations should also review incident response plans to incorporate potential AI infrastructure compromise scenarios.

Description

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.

References

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