CVE-2021-29567: Lack of validation in `SparseDenseCwiseMul`
First published: Fri May 14 2021(Updated: )
### Impact Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data: ```python import tensorflow as tf indices = tf.constant([], shape=[10, 0], dtype=tf.int64) values = tf.constant([], shape=[0], dtype=tf.int64) shape = tf.constant([0, 0], shape=[2], dtype=tf.int64) dense = tf.constant([], shape=[0], dtype=tf.int64) tf.raw_ops.SparseDenseCwiseMul( sp_indices=indices, sp_values=values, sp_shape=shape, dense=dense) ``` Since the [implementation](https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no [constraints between dimensions](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. ### Patches We have patched the issue in GitHub commit [7ae2af34087fb4b5c8915279efd03da3b81028bc](https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| pip/tensorflow-gpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
| pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
| pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow | <2.1.4 | 2.1.4 |
| TensorFlow Keras | <2.1.4 | |
| TensorFlow Keras | >=2.2.0<2.2.3 | |
| TensorFlow Keras | >=2.3.0<2.3.3 | |
| TensorFlow Keras | >=2.4.0<2.4.2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp3c-xw9g-gpcg
- https://nvd.nist.gov/vuln/detail/CVE-2021-29567
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-495.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-693.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-204.yaml
- https://github.com/advisories/GHSA-wp3c-xw9g-gpcg (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29567?
CVE-2021-29567 has been classified with a high severity level due to the potential for denial of service attacks.
How do I fix CVE-2021-29567?
To resolve CVE-2021-29567, upgrade to TensorFlow version 2.1.4, 2.2.3, 2.3.3, or 2.4.2.
Which versions of TensorFlow are affected by CVE-2021-29567?
CVE-2021-29567 affects TensorFlow versions prior to 2.1.4, versions between 2.2.0 and 2.2.3, versions between 2.3.0 and 2.3.3, and versions between 2.4.0 and 2.4.2.
What kind of exploit is associated with CVE-2021-29567?
CVE-2021-29567 can be exploited to trigger denial of service via `CHECK`-fails or out-of-bounds memory accesses.
Is there a specific TensorFlow package that addresses CVE-2021-29567?
Yes, TensorFlow packages such as tensorflow-gpu and tensorflow-cpu provide patched versions to mitigate CVE-2021-29567.
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