VulnerabilityPublished 6 Aug 2025

CVE-2025-23321: NVIDIA Triton Inference Server · Divide By Zero

Affects NVIDIA Triton Inference ServerSource CVE Database V5

Not known to be exploited · CVSS 7.5 High : upgrade to 25.07 soon.

Data as of 26 Oct 2026, 15:59 UTC

CVSS 3.1 base score

7.5High

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.5% chance of exploitation in 30 days – higher than 42% 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:N/A:H

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: None
Integrity: None
Availability: High

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: None: There is no loss of confidentiality within the impacted component.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: High: There is total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).

Definitions: FIRST CVSS v3.1 specification.

Exploitation 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.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC or the CVSS exploit maturity reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

Upgrade NVIDIA Triton Inference Server to 25.07.

Affected software

Product	Affected	Fixed in
NVIDIA Triton Inference Server	Affected <25.07	Fixed in 25.07

AI-suggested

European organizations should prioritize upgrading to NVIDIA Triton Inference Server version 25.07 or later as soon as it becomes available, as this version addresses the divide by zero vulnerability. If you cannot patch yet, organizations should implement network-level protections such as strict input validation and filtering to block malformed or suspicious requests targeting the inference server. Deploying Web Application Firewalls (WAFs) or Intrusion Prevention Systems (IPS) with custom rules to detect anomalous request patterns can reduce exposure. Monitoring server logs for unusual request errors or crashes can provide early warning signs of exploitation attempts. Additionally, isolating the Triton server within segmented network zones and restricting access to trusted clients only will limit the attack surface. Organizations should also prepare incident response plans to quickly recover from potential denial of service events, including failover strategies and backup inference capabilities.

Description

NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause a divide by zero issue by issuing an invalid request. A successful exploit of this vulnerability might lead to denial of service.

Impact

For European organizations, the impact of this vulnerability can be substantial, especially for those relying on NVIDIA Triton Inference Server for AI-driven applications such as healthcare diagnostics, autonomous vehicles, financial modeling, and industrial automation. A successful exploit could lead to denial of service, causing interruption of critical AI inference tasks, resulting in operational downtime, loss of productivity, and potential financial losses. In sectors like healthcare or transportation, such disruptions could have safety implications or degrade service quality. Additionally, the availability of AI services is often integral to customer-facing applications, so outages could damage reputation and customer trust. Since the vulnerability does not compromise confidentiality or integrity, data breaches are unlikely, but the service unavailability itself poses a significant risk to business continuity.

References

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 6 Aug 2025, 13:19 UTC

WeaknessesCWE-369

Technical details

References

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