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CVE-2025-33202VulnerabilityPublished Updated

CVE-2025-33202: NVIDIA Triton Inference Server · Stack-based Buffer Overflow

Affects NVIDIA Triton Inference ServerSource CVE Database V5

0

Not known to be exploited · CVSS 6.5 Medium : upgrade to 25.09 when convenient.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.1 base score
6.5Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.4% chance of exploitation in 30 days – higher than 40% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 25.09
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 11 Nov 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 2026-10-25.

What to do

Upgrade NVIDIA Triton Inference Server to 25.09.

Affected software

ProductAffectedFixed in
NVIDIA Triton Inference ServerAffected
  • <25.09
Fixed in
  • 25.09

AI-suggested

1. Apply the official patch or upgrade to NVIDIA Triton Inference Server version 25.09 or later as soon as it becomes available. 2. Until patches are deployed, restrict network access to the Triton server using firewalls or network segmentation to limit exposure to trusted users and systems only. 3. Implement input validation and payload size restrictions at the network perimeter or application gateway to block abnormally large requests. 4. Monitor server logs and network traffic for unusual payload sizes or repeated malformed requests that could indicate exploitation attempts. 5. Employ runtime protections such as stack canaries or address space layout randomization (ASLR) if supported by the deployment environment to mitigate buffer overflow impacts. 6. Conduct regular security assessments and penetration testing focused on AI infrastructure to identify and remediate similar vulnerabilities proactively. 7. Establish incident response plans specific to AI service disruptions to minimize downtime in case of exploitation.

Description

NVIDIA Triton Inference Server for Linux and Windows contains a vulnerability where an attacker could cause a stack overflow by sending extra-large payloads. A successful exploit of this vulnerability might lead to denial of service.

References

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