Integer division by 0 in sparse reshaping
Medium5.5CVE-2021-37640 · Published Aug 25, 2021 · updated Jul 8, 2026
### Impact The implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception: ```python import tensorflow as tf tf.raw_ops.SparseReshape( input_indices = np.ones((1,3)), input_shape = np.array([1,1,0]), new_shape = np.array([1,0])) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. ### Patches We have patched the issue in GitHub commit [4923de56ec94fff7770df259ab7f2288a74feb41](https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5....
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | < 2.3.4 | 2.3.4 |
| >= 2.4.0, < 2.4.3 | 2.4.3 | |
| >= 2.5.0, < 2.5.1 | 2.5.1 |
Details and references
### Impact The implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception: ```python import tensorflow as tf tf.raw_ops.SparseReshape( input_indices = np.ones((1,3)), input_shape = np.array([1,1,0]), new_shape = np.array([1,0])) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. ### Patches We have patched the issue in GitHub commit [4923de56ec94fff7770df259ab7f2288a74feb41](https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
- CVSS 3.1
- CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-369
- Also known as
- BIT-tensorflow-2021-37640, CVE-2021-37640, PYSEC-2021-262, PYSEC-2021-553, PYSEC-2021-751
- github.com/tensorflow/tensorflow/security/advisories/GHSA-95xm-g58g-3p88
- nvd.nist.gov/vuln/detail/CVE-2021-37640
- github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-553.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-751.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-262.yaml
- github.com/tensorflow/tensorflow
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