CVE-2021-41206
published 2021-11-05CVE-2021-41206: TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the…
PriorityP338high7.8CVSS 3.1
AVLACLPRLUINSUCHIHAH
EPSS
0.17%
7.1th percentile
TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Affected
20 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| debian | tensorflow | — | — |
| tensorflow | — | — | |
| tensorflow | >= 2.4.0 < 2.4.4 | 2.4.4 | |
| tensorflow | >= 2.5.0 < 2.5.2 | 2.5.2 | |
| tensorflow | >= 2.6.0 < 2.6.1 | 2.6.1 | |
| intel | optimization_for_tensorflow | < 4d74d8a00b07441cba090a02e0dd9ed385145bf4 | 4d74d8a00b07441cba090a02e0dd9ed385145bf4 |
| intel | optimization_for_tensorflow | < 579261dcd446385831fe4f7457d802a59685121d | 579261dcd446385831fe4f7457d802a59685121d |
| intel | optimization_for_tensorflow | < e7f497570abb6b4ae5af4970620cd880e4c0c904 | e7f497570abb6b4ae5af4970620cd880e4c0c904 |
| intel | optimization_for_tensorflow | < da4aad5946be30e5f049920fa076e1f7ef021261 | da4aad5946be30e5f049920fa076e1f7ef021261 |
| intel | optimization_for_tensorflow | < 4dddb2fd0b01cdd196101afbba6518658a2c9e07 | 4dddb2fd0b01cdd196101afbba6518658a2c9e07 |
| intel | optimization_for_tensorflow | >= 0 < 2.5.3 | 2.5.3 |
| intel | optimization_for_tensorflow | >= 0 < 2.4.4 | 2.4.4 |
| intel | optimization_for_tensorflow | >= 0 < 68422b215e618df5ad375bcdc6d2052e9fd3080a | 68422b215e618df5ad375bcdc6d2052e9fd3080a |
| intel | optimization_for_tensorflow | >= 2.5.0 < 2.5.2 | 2.5.2 |
| intel | optimization_for_tensorflow | >= 2.6.0 < 2.6.3 | 2.6.3 |
| intel | optimization_for_tensorflow | >= 2.6.0 < 2.6.1 | 2.6.1 |
| intel | optimization_for_tensorflow | >= 2.7.0 < 2.7.1 | 2.7.1 |
| tensorflow | tensorflow | < 2.4.4 | 2.4.4 |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
CVSS provenance
nvdv3.17.8HIGHCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
nvdv2.04.6MEDIUMAV:L/AC:L/Au:N/C:P/I:P/A:P
ghsa7.8HIGH
osv7.8HIGH
vendor_debian7.0LOW
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OSV
Improper Validation of Integrity Check Value in TensorFlow
osv·2022-02-09·CVSS 7.8
[HIGH] Improper Validation of Integrity Check Value in TensorFlow
Improper Validation of Integrity Check Value in TensorFlow
### Impact
The implementation of [`tf.sparse.split`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_split_op.cc#L26-L102) does not fully validate the input arguments. Hence, a malicious user can trigger a denial of service via a segfault or a heap OOB read:
```python
import tensorflow as tf
data = tf.random.uniform([1, 32, 32], dtype=tf.float32)
axis = [1, 2]
x = tf.sparse.from_dense(data)
result = tf.sparse.split(x,3, axis=axis)
```
The code assumes `axis` is a scalar. This is another instance of [TFSA-2021-190](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-190.md) (CVE-2021-41206).
### Patches
We have patched th
GHSA
Improper Validation of Integrity Check Value in TensorFlow
ghsa·2022-02-09·CVSS 7.8
[HIGH] CWE-354 Improper Validation of Integrity Check Value in TensorFlow
Improper Validation of Integrity Check Value in TensorFlow
### Impact
The implementation of [`tf.sparse.split`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_split_op.cc#L26-L102) does not fully validate the input arguments. Hence, a malicious user can trigger a denial of service via a segfault or a heap OOB read:
```python
import tensorflow as tf
data = tf.random.uniform([1, 32, 32], dtype=tf.float32)
axis = [1, 2]
x = tf.sparse.from_dense(data)
result = tf.sparse.split(x,3, axis=axis)
```
The code assumes `axis` is a scalar. This is another instance of [TFSA-2021-190](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-190.md) (CVE-2021-41206).
### Patches
We have patched th
GHSA
Incomplete validation of shapes in multiple TF ops
ghsa·2021-11-10
CVE-2021-41206 [HIGH] CWE-354 Incomplete validation of shapes in multiple TF ops
Incomplete validation of shapes in multiple TF ops
### Impact
Several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible.
We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues.
### Patches
We have patched the issue in GitHub commits [68422b215e618df5ad375bcdc6d2052e9fd3080a](https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a), [4d74d8a00b07441cba090a02e0dd9ed385145bf4](ht
OSV
Incomplete validation of shapes in multiple TF ops
osv·2021-11-10
CVE-2021-41206 [HIGH] Incomplete validation of shapes in multiple TF ops
Incomplete validation of shapes in multiple TF ops
### Impact
Several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible.
We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues.
### Patches
We have patched the issue in GitHub commits [68422b215e618df5ad375bcdc6d2052e9fd3080a](https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a), [4d74d8a00b07441cba090a02e0dd9ed385145bf4](ht
OSV
CVE-2021-41206: TensorFlow is an open source platform for machine learning
osv·2021-11-05
CVE-2021-41206 CVE-2021-41206: TensorFlow is an open source platform for machine learning
TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Debian
CVE-2021-41206: tensorflow - TensorFlow is an open source platform for machine learning. In affected versions...
vendor_debian·2021·CVSS 7.0
CVE-2021-41206 [HIGH] CVE-2021-41206: tensorflow - TensorFlow is an open source platform for machine learning. In affected versions...
TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Scope: local
No detection rules found.
No public exploits indexed.
https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121dhttps://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080ahttps://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121dhttps://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080ahttps://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69
2021-11-05
Published