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VulnerabilityPublished

CVE-2025-23318: NVIDIA Triton Inference Server · Buffer Access with Incorrect Length Value

Affects NVIDIA Triton Inference ServerSource CVE Database V5

0

Not known to be exploited · CVSS 8.1 High : upgrade to 25.07 soon.

Data as of 26 Oct 2026, 17:16 UTC

CVSS 3.1 base score
8.1High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.7% chance of exploitation in 30 days – higher than 54% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 25.07
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 6 Aug 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 25.07.

Affected software

ProductAffectedFixed in
NVIDIA Triton Inference ServerAffected
  • <25.07
Fixed in
  • 25.07

AI-suggested

European organizations should prioritize upgrading NVIDIA Triton Inference Server to version 25.07 or later, where this vulnerability is addressed. Until patching is possible, organizations should implement network-level controls to restrict access to Triton Inference Server instances, limiting exposure to trusted networks and users only. Employing strict firewall rules and network segmentation can reduce the risk of remote exploitation. Monitoring and logging of Triton server activity should be enhanced to detect anomalous behaviors indicative of exploitation attempts. Additionally, organizations should review and harden the configurations of the Python backend, disabling or restricting unnecessary functionalities where feasible. Incorporating runtime application self-protection (RASP) or endpoint detection and response (EDR) solutions capable of detecting memory corruption attempts can provide additional defense layers. Finally, organizations should conduct thorough security assessments of AI infrastructure and integrate this vulnerability into their incident response plans to ensure rapid containment if exploitation is detected.

Description

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write. A successful exploit of this vulnerability might lead to code execution, denial of service, data tampering, and information disclosure.

References

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