VulnerabilityPublished 28 Oct 2026

CVE-2026-115920: ONNX · Out-of-bounds read in ConvTranspose shape inference via undersized weight rank

Affects onnxSource GCVE Database

Authoritative record:CVE-2026-115920· Medium 6.2

Not known to be exploited · CVSS 6.2 Medium : upgrade to 1.22.0 when convenient.

Data as of 28 Oct 2026, 22:12 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

Upgrade onnx to 1.22.0.

Affected software

Product	Affected	Fixed in
PyPIonnx	Affected >=1.2.1 <1.22.0	Fixed in 1.22.0

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

Description

## Summary ONNX ConvTranspose shape inference performed an out-of-bounds read when the weight tensor had fewer than three dimensions and the model did not provide a `kernel_shape` attribute. The inference function derived an empty `effective_kernel_shape` vector from the undersized weight tensor, then indexed that vector once for each spatial dimension of the input tensor. A crafted model passed `onnx.checker.check_model()` and caused SIGSEGV when passed to `onnx.shape_inference.infer_shapes()`. ## Technical details For an input shape such as `[1, 1, 5]`, shape inference computes one spatial input dimension. A weight shape such as `[1, 3]` has no spatial dimensions, so deriving `kernel_shape` from dimensions 2 onward produces an empty vector. The output loop nevertheless accesses `effective_kernel_shape[0]`. The defect affected both the older `convTransposeShapeInference_opset11()` path and the then-current ConvTranspose inference implementation. ## Impact An application that performs shape inference on an untrusted model can be terminated, causing denial of service. The observed result was a deterministic process crash. No confidentiality or integrity impact was demonstrated. ## Affected versions - Affected: ONNX 1.2.1 through 1.21.0 - Patched: ONNX 1.22.0 ## Remediation Upgrade to ONNX 1.22.0 or later. The fix rejects input and weight tensors with fewer than three dimensions before deriving the spatial kernel shape. - Fix: [onnx/onnx#7997](https://github.com/onnx/onnx/pull/7997) - Commit: [`2ec288908`](https://github.com/onnx/onnx/commit/2ec288908952eea9dcf48d4b197915e1f72c6abf) The original malformed model was re-tested against the published ONNX 1.23.2 wheel and correctly raises `InferenceError`. ## Severity The vulnerable ONNX component processes the malicious model through a local library API or file boundary and is not itself bound to a network stack. Applications that expose ONNX model processing over a network should calculate an Environmental score with Modified Attack Vector set to Network.

CWE-125Out-of-bounds Read (opens in a new tab)
Base weaknessCWE Top 25 (2025): #8
The product reads data past the end, or before the beginning, of the intended buffer.
Implementation · Input Validation
Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
Architecture and Design · Language Selection
Use a language that provides appropriate memory abstractions.
OWASP Cheat Sheets
XML Security (opens in a new tab)
Mass Assignment (opens in a new tab)
OWASP ASVS 4.0.3
V13.3.1 (opens in a new tab)
V5.1.4 (opens in a new tab)
V5.1.3 (opens in a new tab)
Attack patterns
Overread Buffers (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

WeaknessesCWE-125

Technical details

References

Community reviews

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