TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
The product does not properly acquire or release a lock on a resource, leading to unexpected resource state changes and behaviors.
The product utilizes multiple threads or processes to allow temporary access to a shared resource that can only be exclusive to one process at a time, but it does not properly synchronize these actions, which might cause simultaneous accesses of this resource by multiple threads or processes.
| Link | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf | third party advisory |
| https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7 | third party advisory patch |