GHSA-x9j7-x98r-r4w2 Segmentation fault in tensorflow-lite

• PyPI tensorflow: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow: >= 2.3.0, < 2.3.1 → fixed in 2.3.1 • PyPI tensorflow-cpu: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow-cpu: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow-cpu: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow-gpu: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow-gpu: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow-gpu: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.1 → fixed in 2.3.1 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.1 → fixed in 2.3.1

Impact If a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. Patches We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. Workarounds A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors). For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been discovered from a variant analysis of GHSA-cvpc-8phh-8f45.