CVE-2021-37641: Heap OOB in `RaggedGather` in TensorFlow
First published: Thu Aug 12 2021(Updated: )
### Impact If the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. ```python import tensorflow as tf tf.raw_ops.RaggedGather( params_nested_splits = [0,0,0], params_dense_values = [1,1], indices = [0,0,9,0,0], OUTPUT_RAGGED_RANK=0) ``` In debug mode, the same code triggers a `CHECK` failure. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. ### Patches We have patched the issue in GitHub commit [a2b743f6017d7b97af1fe49087ae15f0ac634373](https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373). 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
| 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/a2b743f6017d7b97af1fe49087ae15f0ac634373
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-vvrj-w2p8
- https://nvd.nist.gov/vuln/detail/CVE-2021-37641
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-554.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-752.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-263.yaml
- https://github.com/advisories/GHSA-9c8h-vvrj-w2p8 (Advisory)
Frequently Asked Questions
What is CVE-2021-37641?
CVE-2021-37641 is a vulnerability in TensorFlow that could allow an attacker to read from outside the bounds of heap allocated buffers.
Which versions of TensorFlow are affected by CVE-2021-37641?
Versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, 2.5.0, 2.6.0-rc0, 2.6.0-rc1, and 2.6.0-rc2 of TensorFlow are affected by CVE-2021-37641.
What is the severity of CVE-2021-37641?
CVE-2021-37641 has a severity rating of 7.1, which is considered high.
How can I fix CVE-2021-37641?
To fix CVE-2021-37641, update TensorFlow to a version that is not affected by the vulnerability (2.3.5 or above, 2.4.4 or above, or 2.6.0 or above).
Where can I find more information about CVE-2021-37641?
You can find more information about CVE-2021-37641 in the TensorFlow GitHub repository and the associated security advisories: [link1](https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373), [link2](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-vvrj-w2p8).
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