Vulnerability · Published Aug 12, 2021 · Assigned by GitHub_M
CVE-2021-37647Null pointer dereference in `SparseTensorSliceDataset` in TensorFlow
High 7.7
In plain English
A high-severity null pointer dereference in tensorflow from tensorflow. There are no confirmed reports of exploitation so far. The chance it is exploited in the next 30 days is estimated at 0.2% (higher than 5% of all known vulnerabilities). Fixed versions are available - upgrade the affected package.
Official description
TensorFlow is an end-to-end open source platform for machine learning. 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. 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, 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. 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). We have patched the issue in GitHub 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.
Affected packages & fixed versions
| Ecosystem | Package | Fixed in | Advisory |
|---|---|---|---|
| PyPI | tensorflow | 2.3.4, 2.4.3 | PYSEC-2021-269 |
| PyPI | tensorflow | 2.3.4 | GHSA-c5x2-p679-95wc |
| PyPI | tensorflow | 2.4.3 | GHSA-c5x2-p679-95wc |
| PyPI | tensorflow | 2.5.1 | GHSA-c5x2-p679-95wc |
| PyPI | tensorflow-cpu | 2.4.3 | GHSA-c5x2-p679-95wc |
| PyPI | tensorflow-cpu | 2.3.4, 2.4.3 | PYSEC-2021-560 |
| PyPI | tensorflow-cpu | 2.3.4 | GHSA-c5x2-p679-95wc |
| PyPI | tensorflow-cpu | 2.5.1 | GHSA-c5x2-p679-95wc |
| PyPI | tensorflow-gpu | 2.3.4, 2.4.3 | PYSEC-2021-758 |
| PyPI | tensorflow-gpu | 2.3.4 | GHSA-c5x2-p679-95wc |
| PyPI | tensorflow-gpu | 2.4.3 | GHSA-c5x2-p679-95wc |
| PyPI | tensorflow-gpu | 2.5.1 | GHSA-c5x2-p679-95wc |
Affected products (as reported)
| Vendor | Product | Versions |
|---|---|---|
| tensorflow | tensorflow | >= 2.5.0, < 2.5.1>= 2.4.0, < 2.4.3< 2.3.4 |
Weakness: CWE-476 · Vector: CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:H