CVE-2021-37647
HIGH7.7EPSS 0.04%Null pointer dereference in `SparseTensorSliceDataset`
Description
### Impact When a user does not supply arguments that determine a valid sparse tensor, `tf.raw_ops.SparseTensorSliceDataset` implementation can be made to dereference a null pointer: ```python import tensorflow as tf tf.raw_ops.SparseTensorSliceDataset( indices=[[],[],[]], values=[1,2,3], dense_shape=[3,3]) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either `indices` or `values` are provided for an empty sparse tensor when the other is not. If `indices` is empty (as in the example above), then [code that performs validation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference: ```cc for (int64_t i = 0; i < indices->dim_size(0); ++i) { int64_t next_batch_index = indices->matrix<int64>()(i, 0); ... } ``` If `indices` as provided by the user is empty, then `indices` in the C++ code above is backed by an empty `std::vector`, hence calling `indices->dim_size(0)` results in null pointer dereferencing (same as calling `std::vector::at()` on an empty vector). ### Patches We have patched the issue in GitHub commit [02cc160e29d20631de3859c6653184e3f876b9d7](https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7). 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.
Affected packages (7)
- Bitnami/tensorflow>= 2.3.0, < 2.3.4, >= 2.4.0, < 2.4.3, >= 2.5.0, < 2.5.1
- PyPI/tensorflowfrom 0, < 2.3.4
- PyPI/tensorflowfrom 0, < 02cc160e29d20631de3859c6653184e3f876b9d7 | >= 2.3.0, < 2.3.4, >= 2.4.0, < 2.4.3
- PyPI/tensorflow-cpufrom 0, < 02cc160e29d20631de3859c6653184e3f876b9d7 | >= 2.3.0, < 2.3.4, >= 2.4.0, < 2.4.3
- PyPI/tensorflow-cpufrom 0, < 2.3.4
- PyPI/tensorflow-gpufrom 0, < 02cc160e29d20631de3859c6653184e3f876b9d7 | >= 2.3.0, < 2.3.4, >= 2.4.0, < 2.4.3
- PyPI/tensorflow-gpufrom 0, < 2.3.4
CVSS scores
| Source | Version | Severity | Vector |
|---|---|---|---|
| osv | CVSS 4.0 | — | CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:N/VI:H/VA:H/SC:N/SI:N/SA:N |
| osv | CVSS 3.1 | HIGH7.7 | CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:H |
References (7)
- ADVISORYhttps://nvd.nist.gov/vuln/detail/CVE-2021-37647
- PATCHhttps://github.com/tensorflow/tensorflow
- WEBhttps://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-560.yaml
- WEBhttps://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-758.yaml
- WEBhttps://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-269.yaml
- WEBhttps://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7
- WEBhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-c5x2-p679-95wc