CVE-2025-23335: NVIDIA Triton Inference Server · Integer Underflow (Wrap or Wraparound)
Affects NVIDIA Triton Inference ServerSource CVE Database V5
Not known to be exploited · CVSS 4.4 Medium : upgrade to 25.05 when convenient.
Data as of 26 Oct 2026, 08:30 UTC
- EPSS
- 0.5% chance of exploitation in 30 days – higher than 43% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 25.05
CVSS:3.1/AV:N/AC:H/PR:H/UI:N/S:U/C:N/I:N/A:HExploitation evidence
Disclosed: evidence found.
CVE published 6 Aug 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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 25.05.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| NVIDIA Triton Inference Server | Affected
| Fixed in
|
AI-suggested
To mitigate CVE-2025-23335, European organizations should prioritize upgrading NVIDIA Triton Inference Server to version 25.05 or later, where the vulnerability is addressed. Until patching is possible, organizations should implement strict access controls to limit administrative privileges on Triton servers, reducing the risk of exploitation by unauthorized users. Network segmentation and firewall rules should restrict access to the inference server interfaces to trusted hosts and networks only. Monitoring and logging of Triton server activity should be enhanced to detect anomalous model configurations or unusual input patterns that could indicate exploitation attempts. Additionally, organizations can implement input validation and sanitization at the application layer before data reaches the inference server to prevent malformed or malicious inputs. Conducting regular security audits and penetration testing focused on AI infrastructure can help identify similar weaknesses. Finally, establishing incident response plans specific to AI service disruptions will improve readiness to handle potential denial of service events stemming from this or related vulnerabilities.
Description
NVIDIA Triton Inference Server for Windows and Linux and the Tensor RT backend contain a vulnerability where an attacker could cause an underflow by a specific model configuration and a specific input. A successful exploit of this vulnerability might lead to denial of service.