CVE-2021-37651: Heap buffer overflow in `FractionalAvgPoolGrad` in TensorFlow
First published: Thu Aug 12 2021(Updated: )
### Impact The implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers: ```python import tensorflow as tf tf.raw_ops.FractionalAvgPoolGrad( orig_input_tensor_shape=[0,1,2,3], out_backprop = np.array([[[[541],[541]],[[541],[541]]]]), row_pooling_sequence=[0, 0, 0, 0, 0], col_pooling_sequence=[-2, 0, 0, 2, 0], overlapping=True) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. ### Patches We have patched the issue in GitHub commit [0f931751fb20f565c4e94aa6df58d54a003cdb30](https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. ### 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 |
|---|---|---|
| Google TensorFlow | >=2.3.0<2.3.4 | |
| Google TensorFlow | >=2.4.0<2.4.3 | |
| Google TensorFlow | =2.5.0 | |
| Google TensorFlow | =2.6.0-rc0 | |
| Google TensorFlow | =2.6.0-rc1 | |
| Google TensorFlow | =2.6.0-rc2 | |
| pip/tensorflow-gpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-gpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-gpu | <2.3.4 | 2.3.4 |
| pip/tensorflow-cpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-cpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-cpu | <2.3.4 | 2.3.4 |
| pip/tensorflow | =2.5.0 | 2.5.1 |
| pip/tensorflow | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow | <2.3.4 | 2.3.4 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr
- https://nvd.nist.gov/vuln/detail/CVE-2021-37651
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-564.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-762.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-273.yaml
- https://github.com/advisories/GHSA-hpv4-7p9c-mvfr (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-37651?
CVE-2021-37651 has a severity rating of Medium due to its potential to cause exploitation through out-of-bounds access.
How do I fix CVE-2021-37651?
To remedy CVE-2021-37651, upgrade TensorFlow to version 2.5.1 or later, or to version 2.4.3 if using a vulnerable version between 2.4.0 and 2.4.3.
What versions of TensorFlow are affected by CVE-2021-37651?
CVE-2021-37651 affects TensorFlow versions 2.3.0 through 2.3.4, 2.4.0 through 2.4.3, and 2.5.0, including release candidates of 2.6.0.
What component of TensorFlow is impacted by CVE-2021-37651?
CVE-2021-37651 impacts the tf.raw_ops.FractionalAvgPoolGrad operation, which can lead to accessing data outside of allocated buffers.
Is there a patch for CVE-2021-37651?
Yes, TensorFlow has released patches in version 2.5.1 and 2.4.3 to fix the vulnerabilities related to CVE-2021-37651.
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