Open Neural Network Exchange (ONNX) is an open standard for machine learning interoperability.

Summary ONNX shape inference did not safely validate the required Scan numscaninputs attribute. Two malformed values could terminate or disrupt the process: 1. If the attribute was missing, ctx.getAttribute("numscaninputs") returned null and was dereferenced. 2. If the attribute was negative, conversion to sizet produced a very large value that reached a vector allocation and raised std::lengtherror. The negative-value variant passed onnx.checker.checkmodel() before reaching shape inference. Technical details The vulnerable Scan inference implementations used the following pattern: cpp auto numscaninputs = narrowcast (ctx.getAttribute("numscaninputs")->i()); This code neither checked the returned pointer nor rejected a negative signed value before converting it to an unsigned size. The pattern was present in the current Scan inference function and the older opset 8 and 9 paths. The fix uses the required-attribute helper and checked narrowing: cpp auto numscaninputs = narrow (getRequiredAttributeInt(ctx, "numscaninputs")); Impact An application that performs shape inference on an untrusted model can be terminated or forced into an exceptional allocation path, causing denial of…

curl https://cve.zerocrew.in/api/cve/CVE-2026-115921