CVE-2022-21731: Access of Resource Using Incompatible Type
(updated )
Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for ConcatV2
can be used to trigger a denial of service attack via a segfault caused by a type confusion. The axis
argument is translated into concat_dim
in the ConcatShapeHelper
helper function. Then, a value for min_rank
is computed based on concat_dim
. This is then used to validate that the values
tensor has at least the required rank. However, WithRankAtLeast
receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that min_rank
is a 32-bits value and the value of axis
, the rank
argument is a negative value, so the error check is bypassed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
References
- github.com/advisories/GHSA-m4hf-j54p-p353
- github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/common_shape_fns.cc
- github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.cc
- github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022
- github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353
- nvd.nist.gov/vuln/detail/CVE-2022-21731
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