CVE-2021-29515: Reference binding to null pointer in `MatrixDiag*` ops
First published: Fri May 14 2021(Updated: )
### Impact The implementation of [`MatrixDiag*` operations](https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty: ```cc num_rows = context->input(2).flat<int32>()(0); num_cols = context->input(3).flat<int32>()(0); padding_value = context->input(4).flat<T>()(0); ``` Thus, users can trigger null pointer dereferences if any of the above tensors are null: ```python import tensorflow as tf d = tf.convert_to_tensor([],dtype=tf.float32) p = tf.convert_to_tensor([],dtype=tf.float32) tf.raw_ops.MatrixDiagV2(diagonal=d, k=0, num_rows=0, num_cols=0, padding_value=p) ``` Changing from `tf.raw_ops.MatrixDiagV2` to `tf.raw_ops.MatrixDiagV3` still reproduces the issue. ### Patches We have patched the issue in GitHub commit [a7116dd3913c4a4afd2a3a938573aa7c785fdfc6](https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6). 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 Ye Zhang and Yakun Zhang 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/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hc6c-75p4-hmq4
- https://nvd.nist.gov/vuln/detail/CVE-2021-29515
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-443.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-641.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-152.yaml
- https://github.com/advisories/GHSA-hc6c-75p4-hmq4 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29515?
CVE-2021-29515 is considered a medium severity vulnerability due to its potential to cause runtime errors in certain operations.
How does CVE-2021-29515 affect TensorFlow users?
CVE-2021-29515 affects TensorFlow users by allowing non-validation of empty tensor arguments during certain matrix operations, potentially leading to unexpected behavior.
How do I fix CVE-2021-29515?
To fix CVE-2021-29515, upgrade to TensorFlow version 2.1.4 or later, or ensure that you are using versions 2.2.0 through 2.4.2.
What are the affected versions for CVE-2021-29515?
CVE-2021-29515 impacts TensorFlow versions 2.2.0 through 2.4.2, specifically 2.1.4, 2.2.3, 2.3.3, and 2.4.2.
Is CVE-2021-29515 a critical vulnerability?
No, CVE-2021-29515 is not classified as a critical vulnerability but should still be addressed due to the potential impact on application stability.
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