In TensorFlow before version 2.6.0 when running shape functions, some functions (such as MutableHashTableShape) produce extra output information in the form of a ShapeAndType struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. ShapeRefiner is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but it is not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types.
In TensorFlow before version 2.6.0 when running shape functions, some functions (such as MutableHashTableShape) produce extra output information in the form of a ShapeAndType struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. ShapeRefiner is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but it is not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1
Vulmon