ONNX: Null Pointer Dereference in ScanInferenceFunction Due to Missing Required Attribute
- 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- Where the record stands: published, rejected by its publisher, or superseded by another id.What do these mean?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
Summary
The ONNX machine learning interoperability library (versions prior to the fix) is affected through its Scan operator shape inference code. A maliciously crafted model can crash or disrupt the software during shape inference, causing 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 1.4.0 until 1.22.0, ScanInferenceFunction, ScanInferenceFunction_opset8, and ScanInferenceFunction_opset9 do not safely validate the required num_scan_inputs attribute. A missing attribute is dereferenced as a null pointer, while a negative value is converted to size_t and can reach an exceptional oversized allocation path. An untrusted model can therefore terminate or disrupt shape inference, including a negative-value model that can pass onnx.checker.check_model(). This issue is fixed in version 1.22.0.
Timeline
- CVE published
- First seen by cve.now
Affected versions
- onnx onnx
From 1.4.0 · fixed in 1.22.0
- onnxPyPI
From 1.4.0 · fixed in 1.22.0