VulnerabilityPublished 24 Mar 2026

CVE-2026-24158: NVIDIA Triton Inference Server · Excessive Memory Allocation

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, 11:52 UTC

CVSS 3.1 base score

7.5High

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

EPSS: 0.4% chance of exploitation in 30 days – higher than 35% 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 30 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-2026-24158, organizations should upgrade NVIDIA Triton Inference Server to version 26.01 or later as soon as the patch becomes available. In the interim, implement strict input validation and limit the maximum allowed size for compressed payloads at the HTTP endpoint to prevent excessive memory allocation attempts. Deploy network-level protections such as rate limiting, web application firewalls (WAFs), and anomaly detection systems to identify and block suspicious large payload requests. Monitor server resource usage and HTTP request patterns to detect early signs of exploitation attempts. Consider isolating Triton servers in segmented network zones with restricted access to reduce exposure. Employ runtime memory monitoring tools to detect abnormal allocation spikes. Additionally, review and harden server configurations to minimize attack surface, including disabling unnecessary HTTP endpoints if possible. Maintain up-to-date incident response plans to quickly address potential DoS incidents. Regularly audit and test the environment for similar memory allocation vulnerabilities.

Description

NVIDIA Triton Inference Server contains a vulnerability in the HTTP endpoint where an attacker may cause a denial of service by providing a large compressed payload. A successful exploit of this vulnerability may lead to denial of service.

Impact

The primary impact of CVE-2026-24158 is denial of service, which can disrupt AI inference services relying on NVIDIA Triton Inference Server. Organizations deploying AI models in production environments may experience service outages, degraded performance, or crashes, affecting business-critical applications such as autonomous systems, real-time analytics, and cloud-based AI services. This disruption can lead to operational downtime, loss of customer trust, and potential financial losses. Since the vulnerability is remotely exploitable without authentication, attackers can launch DoS attacks at scale, potentially targeting multiple servers or cloud instances. The impact is particularly severe for organizations with high dependency on AI inference for decision-making or customer-facing services. Additionally, the resource exhaustion could be leveraged as part of a larger multi-vector attack to weaken defenses. Although confidentiality and integrity are not directly affected, the availability impact alone can have cascading effects on organizational operations and service level agreements (SLAs).

CWE-789Memory Allocation with Excessive Size Value (opens in a new tab)
Variant weakness
The product allocates memory based on an untrusted, large size value, but it does not ensure that the size is within expected limits, allowing arbitrary amounts of memory to be allocated.
Implementation · Architecture and Design
Perform adequate input validation against any value that influences the amount of memory that is allocated. Define an appropriate strategy for handling requests that exceed the limit, and consider supporting a configuration option so that the administrator can extend the amount of memory to be used if necessary.
Operation
Run your program using system-provided resource limits for memory. This might still cause the program to crash or exit, but the impact to the rest of the system will be minimized.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
OWASP ASVS 4.0.3
V8.1.4 (opens in a new tab)
V13.4.1 (opens in a new tab)
V11.1.4 (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 24 Mar 2026, 20:46 UTC

WeaknessesCWE-789

Technical details

References

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