Heap buffer overflow in `SparseTensorToCSRSparseMatrix`
Impact
An attacker can trigger a denial of service via a CHECK-fail in converting sparse tensors to CSR Sparse matrices:
```python
import tensorflow as tf
import numpy as np
from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops
indices_array = np.array([[0, 0]])
value_array = np.array([0.0], dtype=np.float32)
dense_shape = [0, 0]
st = tf.SparseTensor(indices_array, value_array, dense_shape)
values_tensor = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(
st.indices, st.values, st.dense_shape)
`
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap:
```cc
csr_row_ptr(indices(i, 0) + 1) += 1;
`
If the value at indices(i, 0) is such that indices(i, 0) + 1 is outside the bounds of csr_row_ptr, this results in writing outside of bounds of heap allocated data.
Patches
We have patched the issue in GitHub commit [1e922ccdf6bf46a3a52641f99fd47d54c1decd13](https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13).
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...
Affected versions
| Package | Affected | Fixed in |
|---|---|---|
| tensorflow PyPI | < 2.1.4 | 2.1.4 |
| >= 2.2.0, < 2.2.3 | 2.2.3 | |
| >= 2.3.0, < 2.3.3 | 2.3.3 | |
| >= 2.4.0, < 2.4.2 | 2.4.2 |
Details and references
### Impact An attacker can trigger a denial of service via a `CHECK`-fail in converting sparse tensors to CSR Sparse matrices: ```python import tensorflow as tf import numpy as np from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops indices_array = np.array([[0, 0]]) value_array = np.array([0.0], dtype=np.float32) dense_shape = [0, 0] st = tf.SparseTensor(indices_array, value_array, dense_shape) values_tensor = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix( st.indices, st.values, st.dense_shape) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap: ```cc csr_row_ptr(indices(i, 0) + 1) += 1; ``` If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. ### Patches We have patched the issue in GitHub commit [1e922ccdf6bf46a3a52641f99fd47d54c1decd13](https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13). 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.
- CVSS 3.1
- CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
- Severity from
- GitHub (reviewed advisory)
- Weakness
- CWE-131
- Also known as
- BIT-tensorflow-2021-29545, CVE-2021-29545, PYSEC-2021-182, PYSEC-2021-473, PYSEC-2021-671
- github.com/tensorflow/tensorflow/security/advisories/GHSA-hmg3-c7xj-6qwm
- nvd.nist.gov/vuln/detail/CVE-2021-29545
- github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-473.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-671.yaml
- github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-182.yaml
- github.com/tensorflow/tensorflow