VulnerabilityPublished 26 Oct 2026Updated 28 Oct 2026

CVE-2026-115921: Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability

Affects onnxSource GCVE Database

Authoritative record:CVE-2026-115921· Medium 6.2

Not known to be exploited · CVSS 6.2 Medium · fixed in 1.22.0 : update onnx when convenient.

Data as of 28 Oct 2026, 22:58 UTC

CVSS 3.1 base score

6.2Medium

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

Patch: Available – fixed in 1.22.0

CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Attack Vector: Local
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: Local: The vulnerable component is not bound to the network stack and the attacker’s path is via read/write/execute capabilities. Either: the attacker exploits the vulnerability by accessing the target system locally (e.g., keyboard, console), or remotely (e.g., SSH); or the attacker relies on User Interaction by another person to perform actions required to exploit the vulnerability (e.g., tricking a legitimate user into opening a malicious document).
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 26 Oct 2026
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

Patches and advisories

security-tracker.debian.org/tracker/cve-2026-115921 (opens in a new tab)

Affected software

Product	Affected	Fixed in
Ubuntu 26.10onnx`pkg:deb/ubuntu/onnx?arch=source&distro=stonking`Other threats	Affected =1.20.0-1 =1.20.0-6	Fixed in–
Ubuntu 26.04 LTSonnx`pkg:deb/ubuntu/onnx?arch=source&distro=resolute`Other threats	Affected =1.17.0-3build1 =1.20.0-1	Fixed in–
Ubuntu Pro 24.04 LTSonnx`pkg:deb/ubuntu/onnx?arch=source&distro=esm-apps/noble`Other threats	Affected 1.13.1-3 – 1.14.1-2.1ubuntu0.1~esm1 7 affected builds	Fixed in–
Ubuntu 22.04 LTSonnx`pkg:deb/ubuntu/onnx?arch=source&distro=jammy`Other threats	Affected 1.7.0+dfsg-3build1 – 1.7.0+dfsg-3ubuntu2 4 affected builds	Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

Upgrade ONNX to version 1.22.0 or later where this vulnerability is fixed. No other mitigations are indicated. Since this is a software vulnerability in a specific library version, patching is the recommended and effective remediation.

Description

A vulnerability in Open Neural Network Exchange (ONNX) versions from 1.4.0 up to but not including 1.22.0 allows untrusted models to cause denial of service by triggering null pointer dereference or excessive memory allocation during shape inference. This occurs due to improper validation of the num_scan_inputs attribute in certain ScanInferenceFunction implementations. The issue is fixed in version 1.22.0.

Impact

An attacker can supply a malicious ONNX model that triggers a null pointer dereference or excessive memory allocation during shape inference, resulting in denial of service (application crash or disruption). There is no impact on confidentiality or integrity reported. The vulnerability requires local access (AV:L) and no privileges (PR:N) or user interaction (UI:N) are needed. The severity is medium with a CVSS score of 6.2.

Summary

The ONNX open standard for machine learning interoperability contains a vulnerability in versions 1.4.0 through 1.21.x where the ScanInferenceFunction, ScanInferenceFunction_opset8, and ScanInferenceFunction_opset9 functions do not properly validate the required num_scan_inputs attribute. If this attribute is missing, it is dereferenced as a null pointer, causing a crash. If the attribute is negative, it is converted to a size_t type, potentially leading to an abnormally large memory allocation. This flaw allows an attacker supplying a crafted untrusted model to disrupt or terminate the shape inference process. The vulnerability is addressed in ONNX version 1.22.0.

References

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