VulnerabilityPublished 24 Mar 2026

CVE-2025-33238: NVIDIA Triton Inference Server · Race Condition

Affects NVIDIA Triton Inference ServerSource CVE Database V5

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Not known to be exploited · CVSS 7.5 High · patch available : patch NVIDIA Triton Inference Server soon.

Data as of 30 Oct 2026, 06:59 UTC

CVSS 3.1 base score

7.5High

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

EPSS: 0.3% chance of exploitation in 30 days – higher than 25% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available

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 24 Mar 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: 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 29 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.
Weaponized: A Metasploit exploit module or Exploit-DB entry exists (Metasploit scanner modules do not count), 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

Affected software

Product	Affected	Fixed in
NVIDIATriton Inference Server	Affected =All versions prior to 26.01	Fixed in–

AI-suggested

To mitigate CVE-2025-33238, organizations should prioritize upgrading NVIDIA Triton Inference Server to version 26.01 or later once the patch is officially released. Until then, restricting network access to the Sagemaker HTTP server component is critical; implement network segmentation and firewall rules to limit exposure to trusted clients only. Employ strict authentication and authorization mechanisms where possible to reduce attack surface. Monitor server logs and performance metrics for signs of abnormal exceptions or crashes indicative of exploitation attempts. Consider deploying rate limiting or request throttling to mitigate rapid concurrent requests that could trigger the race condition. Additionally, review and harden concurrency controls and resource management policies in the deployment environment. For cloud deployments, leverage provider-specific security groups and virtual private clouds to isolate Triton servers. Finally, maintain an incident response plan to quickly address any service disruptions caused by exploitation attempts.

Description

NVIDIA Triton Inference Server Sagemaker HTTP server contains a vulnerability where an attacker may cause an exception. A successful exploit of this vulnerability may lead to denial of service.

Impact

The primary impact of CVE-2025-33238 is denial of service, which can disrupt AI inference services relying on NVIDIA Triton Inference Server. This disruption can affect business-critical applications such as real-time analytics, autonomous systems, and cloud-based AI services, leading to downtime and potential loss of revenue or operational capability. Since the vulnerability does not compromise data confidentiality or integrity, the risk is confined to availability. However, given the increasing reliance on AI inference servers in sectors like finance, healthcare, manufacturing, and autonomous vehicles, even temporary service outages can have cascading effects on dependent systems and processes. Organizations with large-scale deployments or those providing AI inference as a service are particularly vulnerable to operational disruptions. The ease of remote exploitation without authentication increases the threat landscape, especially for publicly accessible Triton servers. This could also be leveraged as part of a broader attack to degrade AI capabilities or cause reputational damage.

CWE-362Concurrent Execution using Shared Resource with Improper Synchronization (Race Condition) (opens in a new tab)
Class weaknessLikelihood of exploit: MediumOWASP Top 10 A06:2025: Insecure Design (opens in a new tab)
The product contains a concurrent code sequence that requires temporary, exclusive access to a shared resource, but a timing window exists in which the shared resource can be modified by another code sequence operating concurrently.
Architecture and Design
In languages that support it, use synchronization primitives. Only wrap these around critical code to minimize the impact on performance.
Architecture and Design
Use thread-safe capabilities such as the data access abstraction in Spring.
Architecture and Design
Minimize the usage of shared resources in order to remove as much complexity as possible from the control flow and to reduce the likelihood of unexpected conditions occurring.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
Secure Design Principles (opens in a new tab)
OWASP ASVS 4.0.3
V1.11.2 (opens in a new tab)
Attack patterns
Leveraging Time-of-Check and Time-of-Use (TOCTOU) Race Conditions (opens in a new tab)
Leveraging Race Conditions (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

Community reviews

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 24 Mar 2026, 20:51 UTC

WeaknessesCWE-362

Technical details

References

Community reviews

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