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 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.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Tensorflow | 2.4.0 (including) | 2.4.4 (excluding) | |
| Tensorflow | 2.5.0 (including) | 2.5.2 (excluding) | |
| Tensorflow | 2.6.0 (including) | 2.6.1 (excluding) | |
| Tensorflow | 2.7.0-rc0 (including) | 2.7.0-rc0 (including) | |
| Tensorflow | 2.7.0-rc1 (including) | 2.7.0-rc1 (including) |
Synchronization refers to a variety of behaviors and mechanisms that allow two or more independently-operating processes or threads to ensure that they operate on shared resources in predictable ways that do not interfere with each other. Some shared resource operations cannot be executed atomically; that is, multiple steps must be guaranteed to execute sequentially, without any interference by other processes. Synchronization mechanisms vary widely, but they may include locking, mutexes, and semaphores. When a multi-step operation on a shared resource cannot be guaranteed to execute independent of interference, then the resulting behavior can be unpredictable. Improper synchronization could lead to data or memory corruption, denial of service, etc.