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VulnerabilityPublished

CVE-2025-23336: NVIDIA Triton Inference Server · Improper Input Validation

Affects NVIDIA Triton Inference ServerSource CVE Database V5

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Not known to be exploited · CVSS 4.4 Medium : upgrade to 25.08 when convenient.

Data as of 31 Oct 2026, 08:20 UTC

CVSS 3.1 base score
4.4Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.3% chance of exploitation in 30 days – higher than 28% 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:H/PR:H/UI:N/S:U/C:N/I:N/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. Weaponized: 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 30 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

To mitigate this vulnerability, European organizations should: 1) Upgrade to NVIDIA Triton Inference Server version 25.08 or later once the patch is released, as this will contain the fix for the improper input validation. 2) Restrict access to the Triton server to only trusted and authenticated users with the minimum necessary privileges to reduce the risk of malicious model uploads. 3) Implement strict network segmentation and firewall rules to limit exposure of the Triton server to untrusted networks or users. 4) Employ monitoring and anomaly detection on model uploads and server behavior to detect attempts to load malformed or suspicious models. 5) Conduct regular security audits and vulnerability scans on AI infrastructure to identify outdated Triton versions and ensure compliance with security policies. 6) Use application whitelisting or model validation tools to verify model integrity before deployment to the Triton server. 7) Maintain incident response plans that include AI infrastructure to quickly respond to potential denial of service events. These steps go beyond generic advice by focusing on access control, proactive monitoring, and operational preparedness specific to AI inference environments.

Description

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause a denial of service by loading a misconfigured model. A successful exploit of this vulnerability might lead to denial of service.

References

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