In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.
The product does not check or incorrectly checks for unusual or exceptional conditions that are not expected to occur frequently during day to day operation of the product.
The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
| Link | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security/advisories/GHSA-977j-xj7q-2jr9 | patch third party advisory exploit |
| https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf | patch |
| https://github.com/tensorflow/tensorflow/releases/tag/v1.15.2 | release notes |
| https://github.com/tensorflow/tensorflow/releases/tag/v2.0.1 | release notes |