| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow-cpu | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow-gpu | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4 | 2.2.32.3.32.4.22.1.4 |
Calling tf.raw_ops.ImmutableConst with a dtype of tf.resource or tf.variant results in a segfault in the implementation as code assumes that the tensor contents are pure scalars.
>>> import tensorflow as tf
>>> tf.raw_ops.ImmutableConst(dtype=tf.resource, shape=[], memory_region_name="/tmp/test.txt")
...
Segmentation fault
We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
If using tf.raw_ops.ImmutableConst in code, you can prevent the segfault by inserting a filter for the dtype argument.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
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