VulnerabilityPublished 26 Oct 2026Updated 28 Oct 2026

CVE-2026-115922: Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability

Affects onnxSource GCVE Database

Authoritative record:CVE-2026-115922· 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-2026-115922 (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 mitigation is indicated or required.

Description

CVE-2026-115922 is a vulnerability in the Open Neural Network Exchange (ONNX) prior to version 1.22.0. The issue arises from the _unpack_2bit() and _unpack_4bit() functions resizing unpacked arrays based on attacker-controlled tensor dimensions without verifying that the raw data matches the declared number of elements. This can lead to excessive memory allocation when processing untrusted tensor data, causing memory exhaustion. The vulnerability is fixed in ONNX version 1.22.0.

Impact

An attacker can cause excessive memory allocation by crafting malicious tensor data, leading to denial of service through memory exhaustion. There is no impact on confidentiality or integrity reported. The CVSS v3.1 score is 6.2 (medium severity) with the vector indicating local attack vector, low attack complexity, no privileges required, no user interaction, unchanged scope, no confidentiality or integrity impact, and high availability impact.

Summary

The vulnerability in ONNX versions before 1.22.0 involves the _unpack_2bit() and _unpack_4bit() functions, which resize unpacked arrays according to the TensorProto.dims field controlled by an attacker. These functions do not verify that the raw_data or int32_data fields encode the declared number of elements, allowing an attacker to craft a UINT2, INT2, UINT4, INT4, or FLOAT4E2M1 tensor that expands a small packed payload into a disproportionately large memory allocation. This can lead to memory exhaustion when the tensor is materialized via onnx.numpy_helper.to_array() or onnx.reference.ReferenceEvaluator. The issue is resolved in version 1.22.0.

References

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