CVE-2021-41219: Undefined behavior via `nullptr` reference binding in sparse matrix multiplication
First published: Fri Nov 05 2021(Updated: )
### Impact The [code for sparse matrix multiplication](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/sparse_matmul_op.cc#L954-L1086) is vulnerable to undefined behavior via binding a reference to `nullptr`: ```python import tensorflow as tf tf.raw_ops.SparseMatMul( a=[[1.0,1.0,1.0]], b=[[],[],[]], transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=True) ``` This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. ### Patches We have patched the issue in GitHub commit [e6cf28c72ba2eb949ca950d834dd6d66bb01cfae](https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae). The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 | <2.4.4 | |
| Google TensorFlow | >=2.5.0<2.5.2 | |
| Google TensorFlow | =2.6.0 | |
| pip/tensorflow-gpu | <2.4.4 | 2.4.4 |
| pip/tensorflow-gpu | >=2.5.0<2.5.2 | 2.5.2 |
| pip/tensorflow-gpu | >=2.6.0<2.6.1 | 2.6.1 |
| pip/tensorflow-cpu | <2.4.4 | 2.4.4 |
| pip/tensorflow-cpu | >=2.5.0<2.5.2 | 2.5.2 |
| pip/tensorflow-cpu | >=2.6.0<2.6.1 | 2.6.1 |
| pip/tensorflow | <2.4.4 | 2.4.4 |
| pip/tensorflow | >=2.5.0<2.5.2 | 2.5.2 |
| pip/tensorflow | >=2.6.0<2.6.1 | 2.6.1 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x
- https://nvd.nist.gov/vuln/detail/CVE-2021-41219
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-628.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-826.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-411.yaml
- https://github.com/advisories/GHSA-4f99-p9c2-3j8x (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-41219?
CVE-2021-41219 has a CVSS score that indicates it is a high severity vulnerability.
How do I fix CVE-2021-41219?
To fix CVE-2021-41219, update your version of TensorFlow to 2.4.4, 2.5.2, or 2.6.1 depending on your current version.
What software is affected by CVE-2021-41219?
CVE-2021-41219 affects TensorFlow versions prior to 2.4.4, 2.5.0 to 2.5.2, and 2.6.0.
What kind of vulnerability is CVE-2021-41219?
CVE-2021-41219 is a vulnerability that can lead to undefined behavior due to a reference being bound to a null pointer.
Is there a patch available for CVE-2021-41219?
Yes, patches for CVE-2021-41219 are available in the updated TensorFlow releases.
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