ONNX: Out-of-bounds read in ConvTranspose shape inference via undersized weight rank
- Published
- Last modified
- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean? →
- published
- Whether CISA lists it in its Known Exploited Vulnerabilities catalog: proof that attackers are using it.What do these mean? →
- Not listed in CISA KEV
- Fix
- Fixed in 1.22.0
- Whether working attack code is public (Exploit-DB, Metasploit, Nuclei or GitHub). Public code makes attacks easier.What do these mean? →
- None on Exploit-DB, Metasploit or GitHub
- Exploitation: none
- Automatable: no
- Technical impact: partial
Summary
The ONNX (Open Neural Network Exchange) library, used for machine learning model interoperability, is affected. A specially crafted model with an improperly sized convolution weight can cause ONNX's shape inference to crash, resulting in a denial of service.
Summarized by anthropic/claude-haiku-5.5. Verify details against the original sources.
Read the original description
Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability. From 1.2.1 until 1.22.0, ConvTranspose shape inference processes a weight tensor with fewer than three dimensions without a kernel_shape attribute by deriving an empty effective_kernel_shape and indexing it for the input's spatial dimensions. The flaw affects convTransposeShapeInference_opset11() and the current inference path, and an untrusted model that can pass onnx.checker.check_model() can crash onnx.shape_inference.infer_shapes(), causing denial of service without demonstrated confidentiality or integrity impact. This issue is fixed in version 1.22.0.
Timeline
- CVE published
- First seen by cve.now
- CISA SSVC assessment
Exploitation: none · Automatable: no · Technical impact: partial
- Record last updated
Affected versions
- onnx onnx
From 1.2.1 · fixed in 1.22.0
- onnxPyPI
From 1.2.1 · fixed in 1.22.0