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VulnerabilityPublished

CVE-2025-23317: NVIDIA Triton Inference Server · Heap-based Buffer Overflow

Affects NVIDIA Triton Inference ServerSource CVE Database V5

0

Not known to be exploited · CVSS 9.1 Critical : upgrade to 25.07 as a priority.

Data as of 26 Oct 2026, 09:15 UTC

CVSS 3.1 base score
9.1Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
2.0% chance of exploitation in 30 days – higher than 81% 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:L/PR:N/UI:N/S:U/C:N/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 immediately prioritize upgrading NVIDIA Triton Inference Server to version 25.07 or later, where this vulnerability is patched. Until the update can be applied, organizations should implement network-level protections such as restricting access to the Triton HTTP server to trusted internal networks only, using firewalls and network segmentation to limit exposure. Deploy Web Application Firewalls (WAFs) with custom rules to detect and block anomalous HTTP requests targeting the Triton server. Conduct thorough logging and monitoring of Triton server traffic to detect suspicious activity indicative of exploitation attempts. Employ runtime application self-protection (RASP) tools where possible to detect and prevent memory corruption exploits. Additionally, perform regular vulnerability scanning and penetration testing focused on AI infrastructure components. Finally, ensure incident response teams are prepared to handle potential exploitation scenarios involving AI inference servers.

Description

NVIDIA Triton Inference Server contains a vulnerability in the HTTP server, where an attacker could start a reverse shell by sending a specially crafted HTTP request. A successful exploit of this vulnerability might lead to remote code execution, denial of service, data tampering, or information disclosure.

References

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