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CVE-2025-23310VulnerabilityPublished

CVE-2025-23310: NVIDIA Triton Inference Server · Stack-based Buffer Overflow

Affects NVIDIA Triton Inference ServerSource CVE Database V5

0

Not known to be exploited · CVSS 9.8 Critical : upgrade to 25.07 as a priority.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.1 base score
9.8Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
1.8% chance of exploitation in 30 days – higher than 78% 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: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 2026-10-25.

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

1. Immediate application of security patches or updates from NVIDIA once available is paramount. Monitor NVIDIA security advisories and subscribe to vulnerability notifications. 2. In the absence of patches, restrict network exposure of Triton Inference Server instances by implementing strict firewall rules and network segmentation to limit access only to trusted hosts and services. 3. Employ runtime application self-protection (RASP) or host-based intrusion prevention systems (HIPS) to detect and block anomalous behavior indicative of exploitation attempts. 4. Conduct thorough input validation and sanitization on any data fed into the inference server where possible, to reduce the risk of malicious payloads triggering the overflow. 5. Implement robust monitoring and logging of Triton server activities to detect unusual patterns or crashes that may indicate exploitation attempts. 6. Use containerization or sandboxing techniques to isolate the inference server environment, limiting the potential impact of a successful exploit. 7. Review and enforce least privilege principles for service accounts running the Triton server to minimize damage scope if compromised. 8. Prepare incident response plans specifically addressing AI infrastructure compromise scenarios.

Description

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause stack buffer overflow by specially crafted inputs. 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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